Reproducible report:
Phenotypic and molecular characterization of the resistance to strobilurins in Fusarium graminearum
Created: 2021-10-06
Last update: 2022-02-03
R Packages
All packages loaded during this work.
library(readxl)
library(tidyverse)
library(ec50estimator)
library(cowplot)
library(ggthemes)
library(drc)
library(ggridges)
library(epiR)
library(car)
library(gsheet)
library(patchwork)
library(rnaturalearth)
library(ggmap)
library(ggrepel)
library(lme4)
library(lmerTest)
library(ggsn)Isolates
All isolates used in this study with their location were loaded to plotting the map below.
data_load = gsheet2tbl("https://docs.google.com/spreadsheets/d/1YTmns-Mjyz6HWluKN5ksHCpJfD_jshavGVI52oLfszU/edit#gid=1862629108")
data_load## # A tibble: 230 × 10
## Isolate `Cód. Origem isola… Trial `LMX Species ID` Hospedeiro safra município
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 FGT1 07Tr003 DD F.graminearum trigo 2007 Cruz Alta
## 2 FGT2 07Tr004 DD F.graminearum trigo 2007 Cruz Alta
## 3 FGT3 07Tr039 DD F.graminearum trigo 2007 Cruz Alta
## 4 FGT4 07Tr023 DD F.graminearum trigo 2007 Cruz Alta
## 5 FGT5 07Tr037 EC F.graminearum trigo 2007 Cruz Alta
## 6 FGT6 07Tr012 EC F.graminearum trigo 2007 Cruz Alta
## 7 FGT7 07Tr013 EC F.graminearum trigo 2007 Cruz Alta
## 8 5 07Tr070 DD F.graminearum trigo 2007 Cruz Alta
## 9 FGT8 07Tr009 DD F.graminearum trigo 2007 Cruz Alta
## 10 FGT9 07Tr132 DD F.graminearum trigo 2007 Ernestina
## # … with 220 more rows, and 3 more variables: estado <chr>, lat <dbl>,
## # long <dbl>By year and state
map_all = data_load %>%
group_by(safra, estado) %>%
summarise(n = length(Isolate)) ## `summarise()` has grouped output by 'safra'. You can override using the `.groups` argument.map_all## # A tibble: 12 × 3
## # Groups: safra [11]
## safra estado n
## <dbl> <chr> <int>
## 1 2007 RS 23
## 2 2008 RS 29
## 3 2009 RS 27
## 4 2010 RS 28
## 5 2011 PR 6
## 6 2011 RS 23
## 7 2012 PR 6
## 8 2013 PR 6
## 9 2014 PR 7
## 10 2018 PR 62
## 11 2019 PR 6
## 12 2020 PR 7By year in RS
p1 <- map_all %>%
filter(estado=="RS") %>%
ggplot(aes(factor(safra), y= n))+
ylim(0,60)+
geom_col( position = "dodge",width = 0.5, color="black",fill="black", alpha=0.6)+
scale_fill_colorblind()+
theme_minimal()+
labs(x = "Year", y = "Count of Isolates", fill="")
ggsave("img/n_year_rs.png",width = 5, height = 3, dpi = 300)By year in PR
p2 <- map_all %>%
filter(estado=="PR") %>%
ggplot(aes(factor(safra), y= n))+
geom_col( position = "dodge",width = 0.5, color="black",fill="orange", alpha=0.6)+
scale_fill_colorblind()+
theme_minimal()+
labs(x = "Year", y = "Count of Isolates", fill="")
ggsave("img/n_year_pr.png",width = 5, height = 3, dpi = 300)#install.packages("devtools")
#devtools::install_github("ropensci/rnaturalearthhires")
library("rnaturalearth")
BRA <- ne_states(country = "Brazil",
returnclass = "sf")By municipality
map_all_1 = data_load %>%
group_by(estado, município) %>%
summarise(n = length(Isolate),
lat=mean(lat),
long= mean(long)) ## `summarise()` has grouped output by 'estado'. You can override using the `.groups` argument.map_all_1## # A tibble: 36 × 5
## # Groups: estado [2]
## estado município n lat long
## <chr> <chr> <int> <dbl> <dbl>
## 1 PR Campina do Simão 1 -25.1 -51.8
## 2 PR Candói 3 -25.5 -52.0
## 3 PR Foz do Jordão 2 -25.7 -52.1
## 4 PR Goioxim 1 -25.2 -52.0
## 5 PR Goixin 1 -25.2 -52.0
## 6 PR Guarapuava 82 -25.4 -51.5
## 7 PR Pinhão 4 -25.7 -51.7
## 8 PR Pitanga 3 -24.8 -51.8
## 9 PR Reserva do Iguaçu 2 -25.8 -51.9
## 10 PR Turvo 1 -25.0 -51.5
## # … with 26 more rowsMap of isolates
p3 <- ggplot() +
geom_sf(data = BRA, fill = "white",color = "black")+
geom_point(data = map_all_1, aes(as.numeric(long),as.numeric(lat), color= estado, fill= estado, size=n), shape = 21, width = 0.9)+
geom_text_repel(data = map_all_1, aes(as.numeric(long),as.numeric(lat), label=(n)), size = 4, box.padding = 0.2, min.segment.length =1, seed = F, max.overlaps = Inf)+
xlim(-58,-48)+
ylim(-34,-23)+
#scale_size_continuous(breaks = seq(1,10, by = 1), range = c(1, 6))+
#facet_wrap(~safra,nrow = 2)+
theme_map()+
scale_color_manual(values = c("orange","black"))+
scale_fill_manual(values = c("orange","black"))+
guides(color=F, size=F)+
# theme(legend.position = "none")+
labs(fill="", size="", x="", y="", color="" )+
annotate("text", x=-53, y=-24, label="PR", size=4)+
annotate("text", x=-54, y=-30, label="RS", size=4)## Warning: Ignoring unknown parameters: width## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead. p3
ggsave("img/map_new_5.png", dpi= 600, height = 8, width = 8 )library(patchwork)
(p3 | (p2/p1)) +
plot_annotation(tag_levels = "A")+
plot_layout(widths = c(2, 1))
ggsave("img/figure1.png", width =8, height =5)In this study, we phenotypically characterize Fusarium graminearum isolates to azoxystrobin and pyraclostrobin.
EC50 - Pyra
Data
This database contains information about all germination tests conducted, divided by: experiment and geographic collection.
germination_assay<- gsheet2tbl("https://docs.google.com/spreadsheets/d/13uMSrA-gLXXhkgTKxrE0MjZX1ewLZGxnTUIpWCD-n64/edit#gid=1516403512") %>%
group_by(Isolate, Dose, Replicates, Trail) %>%
dplyr::mutate(GC = as.numeric(GC)) %>%
dplyr::mutate(Dose = as.numeric(Dose))
germination_assay## # A tibble: 1,200 × 8
## # Groups: Isolate, Dose, Replicates, Trail [1,200]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT05 07Tr037 2007 1 RS 0 1 46
## 2 FGT05 07Tr037 2007 1 RS 0 2 44
## 3 FGT05 07Tr037 2007 1 RS 0.05 1 18
## 4 FGT05 07Tr037 2007 1 RS 0.05 2 24
## 5 FGT05 07Tr037 2007 1 RS 0.5 1 9
## 6 FGT05 07Tr037 2007 1 RS 0.5 2 11
## 7 FGT05 07Tr037 2007 1 RS 1 1 0
## 8 FGT05 07Tr037 2007 1 RS 1 2 0
## 9 FGT05 07Tr037 2007 1 RS 5 1 0
## 10 FGT05 07Tr037 2007 1 RS 5 2 0
## # … with 1,190 more rowsVisualization
germination_assay %>%
ggplot(aes(as.factor(Dose), GC, color=as.factor(Trail)))+
stat_summary(fun.data = "mean_cl_boot",
geom = "errorbar",
width = 0,
size =.5,
position = position_dodge(width=.5))+
stat_summary(fun = "mean",
size = 1.4,
geom = "point",
position = position_dodge(width=.5))+
facet_wrap(~Isolate)+
scale_color_colorblind()+
theme_minimal_hgrid(font_size = 6)+
labs(x = expression(Dose~~μg/ml),
y = "Number of germinated conidia/dose", color="Trial")
Models
Ten isolates were chosen to select the best model that fits all the data set.
Isolates: “09Tr006”
g1<-germination_assay %>%
filter(Isolate=="07Tr037")
g1## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT05 07Tr037 2007 1 RS 0 1 46
## 2 FGT05 07Tr037 2007 1 RS 0 2 44
## 3 FGT05 07Tr037 2007 1 RS 0.05 1 18
## 4 FGT05 07Tr037 2007 1 RS 0.05 2 24
## 5 FGT05 07Tr037 2007 1 RS 0.5 1 9
## 6 FGT05 07Tr037 2007 1 RS 0.5 2 11
## 7 FGT05 07Tr037 2007 1 RS 1 1 0
## 8 FGT05 07Tr037 2007 1 RS 1 2 0
## 9 FGT05 07Tr037 2007 1 RS 5 1 0
## 10 FGT05 07Tr037 2007 1 RS 5 2 0
## # … with 14 more rowsmodel_1=drm(GC~Dose, data=g1, fct = W1.3())
plot(model_1)
summary(model_1)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.414392 0.046065 8.9957 1.195e-08 ***
## d:(Intercept) 46.719086 1.259436 37.0952 < 2.2e-16 ***
## e:(Intercept) 0.069258 0.011948 5.7964 9.394e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 2.516979 (21 degrees of freedom)ED(model_1,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.028599 0.006745mselect(model_1,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -54.60557 117.2111 2.279955e-01 6.335181
## W1.3 -54.60557 117.2111 2.279955e-01 6.335181
## W1.4 -54.50537 119.0107 1.308674e-01 6.596627
## LL.3 -55.81557 119.6311 1.026525e-01 7.007297
## LL2.3 -55.81557 119.6311 1.026525e-01 7.007297
## W2.3 -56.09034 120.1807 8.526332e-02 7.169599
## LL.4 -55.21835 120.4367 7.666541e-02 7.000443
## BC.5 -54.26618 120.5324 5.748197e-02 6.806783
## W2.4 -55.38144 120.7629 6.783868e-02 7.096234
## LL2.5 -54.71011 121.4202 3.909036e-02 7.063310
## LL.5 -54.75607 121.5121 3.757703e-02 7.090414
## LL2.4 -55.81459 121.6292 4.902204e-02 7.357058
## AR.3 -61.11189 130.2238 2.486918e-03 10.895082
## W1.2 -106.35864 218.7173 1.670998e-17 451.400295
## LL.2 -106.35888 218.7178 1.670703e-17 451.409140
## LL2.2 -106.37095 218.7419 1.655656e-17 451.863636
## AR.2 -106.39170 218.7834 1.630123e-17 452.645455
## W2.2 -106.44860 218.8972 1.562089e-17 454.796940
## LL.3u -106.35878 220.7176 5.916175e-18 472.900950
## LL2.3u -106.37095 220.7419 5.862432e-18 473.380952
## W1.3u NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“UEM-2665”
g2<-germination_assay %>%
filter(Isolate=="08Tr005")
g2## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT35 08Tr005 2008 1 RS 0 1 50
## 2 FGT35 08Tr005 2008 1 RS 0 2 49
## 3 FGT35 08Tr005 2008 1 RS 0.05 1 40
## 4 FGT35 08Tr005 2008 1 RS 0.05 2 43
## 5 FGT35 08Tr005 2008 1 RS 0.5 1 11
## 6 FGT35 08Tr005 2008 1 RS 0.5 2 16
## 7 FGT35 08Tr005 2008 1 RS 1 1 4
## 8 FGT35 08Tr005 2008 1 RS 1 2 2
## 9 FGT35 08Tr005 2008 1 RS 5 1 0
## 10 FGT35 08Tr005 2008 1 RS 5 2 0
## # … with 14 more rowsmodel_2=drm((GC)~Dose, data=g2, fct = W1.3())
plot(model_2)
summary(model_2)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 1.086482 0.085787 12.665 2.676e-11 ***
## d:(Intercept) 49.776520 0.853592 58.314 < 2.2e-16 ***
## e:(Intercept) 0.372232 0.020087 18.531 1.712e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 1.766687 (21 degrees of freedom)ED(model_2,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.265649 0.019286mselect(model_2,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -46.11069 100.2214 9.996229e-01 3.121183
## W1.3 -46.11069 100.2214 9.996229e-01 3.121183
## W1.4 -46.10999 102.2200 9.943820e-01 3.277051
## LL.5 -46.10484 104.2097 9.532342e-01 3.448046
## LL2.5 -46.11198 104.2240 9.062661e-01 3.450100
## BC.5 -46.67499 105.3500 3.607314e-01 3.615826
## BC.4 -48.15981 106.3196 2.137401e-01 3.887482
## LL2.3 -49.47962 106.9592 1.577266e-01 4.132804
## LL.3 -49.47963 106.9593 1.577258e-01 4.132807
## LL.4 -48.94877 107.8975 1.182784e-01 4.151660
## LL2.4 -49.47805 108.9561 7.952525e-02 4.338878
## W2.4 -51.89926 113.7985 1.293801e-02 5.308891
## W2.3 -53.41676 114.8335 1.042904e-02 5.737659
## W2.2 -113.28422 232.5684 1.306337e-21 803.909092
## LL.2 -113.28694 232.5739 1.303682e-21 804.090909
## W1.2 -113.28694 232.5739 1.303682e-21 804.090909
## LL2.2 -113.28694 232.5739 1.303682e-21 804.090909
## LL.3u -113.28694 234.5739 4.601771e-22 842.380952
## W1.3u -113.28694 234.5739 4.601771e-22 842.380952
## LL2.3u -113.28694 234.5739 4.601771e-22 842.380952
## AR.2 NA NA NA NA
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“09Tr006”
g3<-germination_assay %>%
filter(Isolate=="09Tr006")
g3## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT63 09Tr006 2009 1 RS 0 1 48
## 2 FGT63 09Tr006 2009 1 RS 0 2 50
## 3 FGT63 09Tr006 2009 1 RS 0.05 1 36
## 4 FGT63 09Tr006 2009 1 RS 0.05 2 40
## 5 FGT63 09Tr006 2009 1 RS 0.5 1 16
## 6 FGT63 09Tr006 2009 1 RS 0.5 2 15
## 7 FGT63 09Tr006 2009 1 RS 1 1 7
## 8 FGT63 09Tr006 2009 1 RS 1 2 8
## 9 FGT63 09Tr006 2009 1 RS 5 1 0
## 10 FGT63 09Tr006 2009 1 RS 5 2 0
## # … with 14 more rowsmodel_3=drm((GC)~Dose, data=g3, fct = LL.4())
plot(model_3)
summary(model_3)##
## Model fitted: Log-logistic (ED50 as parameter) (4 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 1.013245 0.071826 14.1070 7.422e-12 ***
## c:(Intercept) -1.524378 0.914537 -1.6668 0.1111
## d:(Intercept) 49.503805 0.910898 54.3461 < 2.2e-16 ***
## e:(Intercept) 0.235947 0.021766 10.8402 8.012e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 1.83369 (20 degrees of freedom)ED(model_3,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.235947 0.021766mselect(model_3,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## BC.4 -46.41364 102.8273 6.205798e-01 3.361032
## LL.4 -46.41860 102.8372 6.182784e-01 3.362420
## LL.4 -46.41860 102.8372 6.182784e-01 3.362420
## W2.4 -46.86065 103.7213 4.438115e-01 3.488593
## W1.3 -47.98720 103.9744 3.335461e-01 3.649490
## LL.3 -48.22808 104.4562 2.869326e-01 3.723489
## LL2.3 -48.22808 104.4562 2.869326e-01 3.723489
## BC.5 -46.41399 104.8280 3.352004e-01 3.538032
## LL.5 -46.72594 105.4519 2.394279e-01 3.631212
## LL2.5 -46.92579 105.8516 1.955054e-01 3.692191
## W1.4 -47.97277 105.9455 1.927335e-01 3.827361
## LL2.4 -48.22579 106.4516 1.594201e-01 3.908918
## W2.3 -52.06587 112.1317 2.151831e-02 5.126776
## AR.3 -54.92772 117.8554 2.806161e-03 6.507560
## AR.2 -110.80580 227.6116 6.564358e-21 653.897947
## LL.2 -112.62958 231.2592 1.672613e-21 761.227797
## W1.2 -112.63530 231.2706 1.665452e-21 761.590909
## W2.2 -112.63530 231.2706 1.665452e-21 761.590909
## LL2.2 -112.63530 231.2706 1.665452e-21 761.590909
## LL.3u -112.62960 233.2592 5.904242e-22 797.477775
## W1.3u -112.63530 233.2706 5.879029e-22 797.857143
## LL2.3u -112.63530 233.2706 5.879029e-22 797.857143
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“10Tr042”
g4<-germination_assay %>%
filter(Isolate=="10Tr042")
g4## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT103 10Tr042 2010 1 RS 0 1 47
## 2 FGT103 10Tr042 2010 1 RS 0 2 48
## 3 FGT103 10Tr042 2010 1 RS 0.05 1 29
## 4 FGT103 10Tr042 2010 1 RS 0.05 2 31
## 5 FGT103 10Tr042 2010 1 RS 0.5 1 3
## 6 FGT103 10Tr042 2010 1 RS 0.5 2 5
## 7 FGT103 10Tr042 2010 1 RS 1 1 3
## 8 FGT103 10Tr042 2010 1 RS 1 2 3
## 9 FGT103 10Tr042 2010 1 RS 5 1 0
## 10 FGT103 10Tr042 2010 1 RS 5 2 0
## # … with 14 more rowsmodel_4=drm((GC)~Dose, data=g4, fct = W1.3())
plot(model_4)
summary(model_4)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.70312 0.19039 3.693 0.0013502 **
## d:(Intercept) 47.67086 3.47890 13.703 6.073e-12 ***
## e:(Intercept) 0.42582 0.10810 3.939 0.0007516 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 6.988745 (21 degrees of freedom)ED(model_4,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.25284 0.08476mselect(model_4,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -79.11537 166.2307 9.975693e-01 48.84255
## W1.3 -79.11537 166.2307 9.975693e-01 48.84255
## LL.3 -79.42028 166.8406 9.158392e-01 50.09949
## LL2.3 -79.42028 166.8406 9.158392e-01 50.09949
## AR.3 -79.79778 167.5956 7.781925e-01 51.70062
## W2.3 -79.90851 167.8170 7.372425e-01 52.17987
## W1.4 -79.11023 168.2205 9.825433e-01 51.26271
## LL.4 -79.26313 168.5263 8.760917e-01 51.92005
## BC.4 -79.26830 168.5366 8.727006e-01 51.94243
## W2.4 -79.41570 168.8314 7.813652e-01 52.58439
## LL2.4 -79.42009 168.8402 7.787952e-01 52.60365
## LL2.5 -79.11830 170.2366 8.301280e-01 53.99703
## LL.5 -79.12713 170.2543 8.082235e-01 54.03679
## BC.5 -79.26021 170.5204 6.149193e-01 54.63939
## AR.2 -110.54784 227.0957 5.285544e-10 639.99091
## W2.2 -112.30400 230.6080 1.429512e-10 740.85227
## LL.2 -112.30566 230.6113 1.427749e-10 740.95455
## W1.2 -112.30566 230.6113 1.427749e-10 740.95455
## LL2.2 -112.30566 230.6113 1.427749e-10 740.95455
## LL.3u -112.30566 232.6113 5.179721e-11 776.23810
## W1.3u -112.30566 232.6113 5.179721e-11 776.23810
## LL2.3u -112.30566 232.6113 5.179721e-11 776.23810
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“11Tr005”
g5<-germination_assay %>%
filter(Isolate=="11Tr005")
g5## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT125 11Tr005 2011 1 RS 0 1 50
## 2 FGT125 11Tr005 2011 1 RS 0 2 50
## 3 FGT125 11Tr005 2011 1 RS 0.05 1 44
## 4 FGT125 11Tr005 2011 1 RS 0.05 2 47
## 5 FGT125 11Tr005 2011 1 RS 0.5 1 33
## 6 FGT125 11Tr005 2011 1 RS 0.5 2 28
## 7 FGT125 11Tr005 2011 1 RS 1 1 15
## 8 FGT125 11Tr005 2011 1 RS 1 2 8
## 9 FGT125 11Tr005 2011 1 RS 5 1 0
## 10 FGT125 11Tr005 2011 1 RS 5 2 2
## # … with 14 more rowsmodel_5=drm((GC)~Dose, data=g5, fct = W1.3())
plot(model_5)
summary(model_5)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 1.592285 0.185775 8.571 2.68e-08 ***
## d:(Intercept) 48.132350 0.916531 52.516 < 2.2e-16 ***
## e:(Intercept) 0.805420 0.030512 26.397 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 2.416801 (21 degrees of freedom)ED(model_5,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.639818 0.028947mselect(model_5,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## BC.5 -51.56867 115.1373 8.293316e-02 5.436454
## W1.3 -53.63083 115.2617 9.593524e-02 5.840929
## W1.3 -53.63083 115.2617 9.593524e-02 5.840929
## LL2.3 -54.12445 116.2489 6.861431e-02 6.086208
## LL.3 -54.12445 116.2489 6.861431e-02 6.086208
## W2.3 -54.15634 116.3127 6.713569e-02 6.102404
## BC.4 -53.42048 116.8410 5.319588e-02 6.026406
## W1.4 -53.45998 116.9200 5.164281e-02 6.046279
## LL2.4 -54.09923 118.1985 3.197370e-02 6.377102
## LL.4 -54.14657 118.2931 3.085846e-02 6.402308
## W2.4 -54.15179 118.3036 3.073785e-02 6.405094
## LL.5 -53.54598 119.0920 1.518668e-02 6.410277
## LL2.5 -54.12712 120.2542 9.406479e-03 6.728358
## AR.2 -112.34623 230.6925 3.391935e-20 743.463639
## W1.2 -115.64877 237.2975 2.852615e-21 979.000000
## W2.2 -115.64877 237.2975 2.852615e-21 979.000000
## LL2.2 -115.64877 237.2975 2.852615e-21 979.000000
## LL.2 -115.64879 237.2976 2.852570e-21 979.001726
## LL.3u -115.64684 239.2937 1.008535e-21 1025.454204
## W1.3u -115.64877 239.2975 1.007077e-21 1025.619048
## LL2.3u -115.64877 239.2975 1.007077e-21 1025.619048
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“3072”
g6<-germination_assay %>%
filter(Isolate=="3072")
g6## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 189 3072 2012 1 PR 0 1 48
## 2 189 3072 2012 1 PR 0 2 49
## 3 189 3072 2012 1 PR 0.05 1 48
## 4 189 3072 2012 1 PR 0.05 2 47
## 5 189 3072 2012 1 PR 0.5 1 26
## 6 189 3072 2012 1 PR 0.5 2 24
## 7 189 3072 2012 1 PR 1 1 15
## 8 189 3072 2012 1 PR 1 2 13
## 9 189 3072 2012 1 PR 5 1 1
## 10 189 3072 2012 1 PR 5 2 0
## # … with 14 more rowsmodel_6=drm((GC)~Dose, data=g6, fct = W1.3())
plot(model_6)
summary(model_6)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.955422 0.231565 4.1259 0.0004808 ***
## d:(Intercept) 48.136272 1.933934 24.8903 < 2.2e-16 ***
## e:(Intercept) 0.659662 0.064261 10.2653 1.222e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 3.720013 (21 degrees of freedom)ED(model_6,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.44949 0.07144mselect(model_6,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -63.98160 135.9632 4.707859e-01 13.83849
## W1.3 -63.98160 135.9632 4.707859e-01 13.83849
## W1.4 -63.96901 137.9380 2.952085e-01 14.51518
## LL2.3 -65.57769 139.1554 1.731262e-01 15.80714
## LL.3 -65.57769 139.1554 1.731262e-01 15.80714
## LL2.5 -64.01306 140.0261 1.183971e-01 15.33533
## W2.3 -66.03455 140.0691 1.279884e-01 16.42054
## LL.5 -64.12303 140.2461 1.069347e-01 15.47651
## BC.4 -65.24720 140.4944 1.131871e-01 16.14662
## LL.4 -65.49648 140.9930 9.388583e-02 16.48555
## LL2.4 -65.57752 141.1550 8.834906e-02 16.59727
## BC.5 -64.62795 141.2559 6.777163e-02 16.14161
## W2.4 -66.03331 142.0666 6.276848e-02 17.23980
## AR.2 -111.41550 228.8310 1.021459e-15 687.97966
## LL.2 -114.05675 234.1135 1.413257e-16 857.36490
## W1.2 -114.05801 234.1160 1.411929e-16 857.45455
## W2.2 -114.05801 234.1160 1.411929e-16 857.45455
## LL2.2 -114.05801 234.1160 1.411929e-16 857.45455
## LL.3u -114.05601 236.1120 5.013408e-17 898.13642
## W1.3u -114.05801 236.1160 5.005919e-17 898.28571
## LL2.3u -114.05801 236.1160 5.005919e-17 898.28571
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“3708”
g7<-germination_assay %>%
filter(Isolate=="3708")
g7## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 247 3708 2014 1 PR 0 1 44
## 2 247 3708 2014 1 PR 0 2 47
## 3 247 3708 2014 1 PR 0.05 1 39
## 4 247 3708 2014 1 PR 0.05 2 42
## 5 247 3708 2014 1 PR 0.5 1 7
## 6 247 3708 2014 1 PR 0.5 2 13
## 7 247 3708 2014 1 PR 1 1 4
## 8 247 3708 2014 1 PR 1 2 7
## 9 247 3708 2014 1 PR 5 1 0
## 10 247 3708 2014 1 PR 5 2 0
## # … with 14 more rowsmodel_7=drm((GC)~Dose, data=g7, fct = W1.3())
plot(model_7)
summary(model_7)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.821554 0.073187 11.226 2.467e-10 ***
## d:(Intercept) 46.603848 1.232563 37.810 < 2.2e-16 ***
## e:(Intercept) 0.279406 0.026713 10.460 8.770e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 2.492597 (21 degrees of freedom)ED(model_7,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.178849 0.020814mselect(model_7,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## LL.3 -54.23715 116.4743 9.400930e-01 6.143635
## LL2.3 -54.23715 116.4743 9.400929e-01 6.143635
## W1.3 -54.37195 116.7439 8.945073e-01 6.213041
## W1.3 -54.37195 116.7439 8.945073e-01 6.213041
## W2.3 -54.90412 117.8082 6.989563e-01 6.494774
## BC.4 -54.11267 118.2253 9.032853e-01 6.384248
## LL.4 -54.13660 118.2732 8.872202e-01 6.396990
## LL2.4 -54.23665 118.4733 8.230819e-01 6.450548
## W1.4 -54.30320 118.6064 7.830076e-01 6.486422
## W2.4 -54.45979 118.9196 6.962449e-01 6.571618
## LL.5 -54.00746 120.0149 8.331776e-01 6.661596
## LL2.5 -54.00788 120.0158 8.320434e-01 6.661830
## BC.5 -54.12257 120.2451 6.449556e-01 6.725810
## AR.2 -109.20821 224.4164 1.014198e-17 572.388638
## LL.2 -110.29718 226.5944 4.485024e-18 626.761376
## W2.2 -110.29735 226.5947 4.484435e-18 626.770524
## W1.2 -110.29914 226.5983 4.478449e-18 626.863636
## LL2.2 -110.29914 226.5983 4.478449e-18 626.863636
## LL.3u -110.29913 228.5983 1.584725e-18 656.714083
## W1.3u -110.29914 228.5983 1.584721e-18 656.714286
## LL2.3u -110.29914 228.5983 1.584721e-18 656.714286
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“3712”
g8<-germination_assay %>%
filter(Isolate=="3712")
g8## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 233 3712 2014 1 PR 0 1 50
## 2 233 3712 2014 1 PR 0 2 48
## 3 233 3712 2014 1 PR 0.05 1 40
## 4 233 3712 2014 1 PR 0.05 2 37
## 5 233 3712 2014 1 PR 0.5 1 4
## 6 233 3712 2014 1 PR 0.5 2 3
## 7 233 3712 2014 1 PR 1 1 1
## 8 233 3712 2014 1 PR 1 2 2
## 9 233 3712 2014 1 PR 5 1 0
## 10 233 3712 2014 1 PR 5 2 0
## # … with 14 more rowsmodel_8=drm((GC)~Dose, data=g8, fct = W1.3())
plot(model_8)
summary(model_8)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.867186 0.089162 9.7259 3.145e-09 ***
## d:(Intercept) 49.348147 1.466895 33.6412 < 2.2e-16 ***
## e:(Intercept) 0.285151 0.030114 9.4690 4.993e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 2.968135 (21 degrees of freedom)ED(model_8,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.186862 0.024135mselect(model_8,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -58.56256 125.1251 9.546185e-01 8.809827
## W1.3 -58.56256 125.1251 9.546185e-01 8.809827
## LL2.3 -58.63324 125.2665 9.324557e-01 8.861868
## LL.3 -58.63324 125.2665 9.324557e-01 8.861868
## W2.3 -59.22835 126.4567 7.163762e-01 9.312429
## AR.3 -59.27966 126.5593 6.978334e-01 9.352337
## W1.4 -58.51731 127.0346 8.817144e-01 9.215504
## BC.4 -58.52272 127.0454 8.781462e-01 9.219657
## LL.4 -58.55565 127.1113 8.567244e-01 9.244992
## LL2.4 -58.63286 127.2657 8.085254e-01 9.304664
## W2.4 -58.89664 127.7933 6.633973e-01 9.511464
## BC.5 -58.36272 128.7254 8.893913e-01 9.576363
## LL.5 -58.36306 128.7261 8.879820e-01 9.576637
## LL2.5 -58.36646 128.7329 8.748592e-01 9.579351
## AR.2 -110.80434 227.6087 8.116812e-17 653.818206
## W2.2 -111.92859 229.8572 3.496548e-17 718.034096
## LL.2 -111.93030 229.8606 3.492075e-17 718.136356
## W1.2 -111.93030 229.8606 3.492074e-17 718.136364
## LL2.2 -111.93030 229.8606 3.492074e-17 718.136364
## LL.3u -111.93030 231.8606 1.237007e-17 752.333333
## W1.3u -111.93030 231.8606 1.237007e-17 752.333333
## LL2.3u -111.93030 231.8606 1.237007e-17 752.333333
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“UEM-2628”
g9<-germination_assay %>%
filter(Isolate=="UEM-2628")
g9## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 153 UEM-2628 2011 1 PR 0 1 50
## 2 153 UEM-2628 2011 1 PR 0 2 49
## 3 153 UEM-2628 2011 1 PR 0.05 1 48
## 4 153 UEM-2628 2011 1 PR 0.05 2 41
## 5 153 UEM-2628 2011 1 PR 0.5 1 7
## 6 153 UEM-2628 2011 1 PR 0.5 2 11
## 7 153 UEM-2628 2011 1 PR 1 1 7
## 8 153 UEM-2628 2011 1 PR 1 2 10
## 9 153 UEM-2628 2011 1 PR 5 1 0
## 10 153 UEM-2628 2011 1 PR 5 2 0
## # … with 14 more rowsmodel_9=drm((GC)~Dose, data=g9, fct = LL.3())
plot(model_9)
summary(model_9)##
## Model fitted: Log-logistic (ED50 as parameter) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 1.115313 0.112801 9.8874 2.361e-09 ***
## d:(Intercept) 48.384085 1.585945 30.5080 < 2.2e-16 ***
## e:(Intercept) 0.202291 0.030039 6.7343 1.158e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 3.244151 (21 degrees of freedom)ED(model_9,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.202291 0.030039mselect(model_9,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## LL2.3 -60.69664 129.3933 3.256017e-01 10.52452
## LL.3 -60.69664 129.3933 3.256014e-01 10.52452
## LL.3 -60.69664 129.3933 3.256014e-01 10.52452
## W2.3 -60.84150 129.6830 2.974358e-01 10.65234
## W2.4 -59.86821 129.7364 3.446980e-01 10.31359
## LL.4 -60.54289 131.0858 2.078180e-01 10.91006
## BC.4 -60.55797 131.1159 2.054803e-01 10.92378
## LL2.4 -60.69623 131.3925 1.852401e-01 11.05037
## LL.5 -60.40669 132.8134 9.088920e-02 11.35466
## W1.3 -62.50581 133.0116 1.001883e-01 12.23710
## BC.5 -60.55547 133.1109 7.946619e-02 11.49632
## LL2.5 -60.66396 133.3279 7.211532e-02 11.60073
## W1.4 -62.44609 134.8922 4.986185e-02 12.78517
## AR.3 -64.82831 137.6566 2.017403e-02 14.85020
## LL.2 -112.07376 230.1475 3.376745e-17 726.77326
## W1.2 -112.07976 230.1595 3.361617e-17 727.13636
## W2.2 -112.07976 230.1595 3.361617e-17 727.13636
## LL2.2 -112.07976 230.1595 3.361617e-17 727.13636
## LL.3u -112.07379 232.1476 1.196107e-17 761.38311
## W1.3u -112.07976 232.1595 1.190768e-17 761.76190
## LL2.3u -112.07976 232.1595 1.190768e-17 761.76190
## AR.2 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“UEM-3687”
g10<-germination_assay %>%
filter(Isolate=="UEM-3687")
g10## # A tibble: 24 × 8
## # Groups: Isolate, Dose, Replicates, Trail [24]
## `Work Code` Isolate Year Trail Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 201 UEM-3687 2013 1 PR 0 1 50
## 2 201 UEM-3687 2013 1 PR 0 2 50
## 3 201 UEM-3687 2013 1 PR 0.05 1 38
## 4 201 UEM-3687 2013 1 PR 0.05 2 39
## 5 201 UEM-3687 2013 1 PR 0.5 1 9
## 6 201 UEM-3687 2013 1 PR 0.5 2 12
## 7 201 UEM-3687 2013 1 PR 1 1 7
## 8 201 UEM-3687 2013 1 PR 1 2 5
## 9 201 UEM-3687 2013 1 PR 5 1 0
## 10 201 UEM-3687 2013 1 PR 5 2 0
## # … with 14 more rowsmodel_10=drm((GC)~Dose, data=g10, fct = W1.3())
plot(model_10)
summary(model_10)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.754332 0.048928 15.417 6.331e-13 ***
## d:(Intercept) 49.959061 0.920044 54.301 < 2.2e-16 ***
## e:(Intercept) 0.358266 0.023154 15.473 5.904e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 1.853094 (21 degrees of freedom)ED(model_10,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.220390 0.018365mselect(model_10,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -47.25671 102.5134 9.926256e-01 3.433958
## W1.3 -47.25671 102.5134 9.926256e-01 3.433958
## W1.4 -47.25348 104.5070 9.579036e-01 3.604685
## LL2.5 -47.28507 106.5701 7.186805e-01 3.804409
## LL.5 -47.43077 106.8615 5.584845e-01 3.850881
## BC.4 -49.13592 108.2718 2.334383e-01 4.216917
## LL.4 -49.17254 108.3451 2.271136e-01 4.229806
## LL.3 -50.70540 109.4108 1.445396e-01 4.577277
## LL2.3 -50.70540 109.4108 1.445396e-01 4.577277
## BC.5 -49.14231 110.2846 9.191991e-02 4.441225
## LL2.4 -50.70336 111.4067 7.204816e-02 4.805324
## W2.4 -50.80930 111.6186 6.654519e-02 4.847934
## AR.3 -53.64193 115.2839 1.926499e-02 5.846337
## W2.3 -54.21131 116.4226 1.289160e-02 6.130422
## W2.2 -112.40977 230.8195 5.712814e-21 747.410743
## LL.2 -112.41266 230.8253 5.700440e-21 747.590909
## W1.2 -112.41266 230.8253 5.700440e-21 747.590909
## LL2.2 -112.41266 230.8253 5.700440e-21 747.590909
## LL.3u -112.41266 232.8253 2.012757e-21 783.190476
## W1.3u -112.41266 232.8253 2.012757e-21 783.190476
## LL2.3u -112.41266 232.8253 2.012757e-21 783.190476
## AR.2 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NAEC50 Pyra
Calculation of Ec50 for the number of germinated conidia in relation to the dose, being stratified by year and population (RS, PR). The model was chosen through the tests performed above considering <IAC and best curve fit.
Model: W1.3
ec50_ger=estimate_EC50(GC~Dose,
data= germination_assay ,
isolate_col= "Isolate",
strata_col = c("Year", "Population"),
fct=drc::W1.3(),
interval="delta")
ec50_ger## ID Year Population Estimate Std..Error Lower Upper
## 1 07Tr037 2007 RS 0.02859924 0.006744996 0.01457225 0.04262622
## 2 07Tr012 2007 RS 0.51282040 0.018645465 0.47404503 0.55159577
## 3 07Tr013 2007 RS 1.13393712 0.169768089 0.78088505 1.48698919
## 4 07Tr322 2007 RS 0.19047604 0.062835930 0.05980157 0.32115051
## 5 07Tr323 2007 RS 0.38690800 0.055745447 0.27097900 0.50283700
## 6 08Tr003 2008 RS 0.23496327 0.048103578 0.13492640 0.33500014
## 7 08Tr004 2008 RS 0.19360236 0.030907806 0.12932606 0.25787866
## 8 08Tr005 2008 RS 0.26564941 0.019285887 0.22554221 0.30575660
## 9 08Tr012 2008 RS 0.45700327 0.027791562 0.39920755 0.51479898
## 10 08Tr013 2008 RS 0.46222273 0.023727494 0.41287871 0.51156676
## 11 09Tr005 2009 RS 0.36533706 0.053042868 0.25502837 0.47564574
## 12 09Tr006 2009 RS 0.24903667 0.020650154 0.20609232 0.29198102
## 13 09Tr019 2009 RS 0.12990344 0.028674249 0.07027208 0.18953480
## 14 09Tr020 2009 RS 0.15498850 0.011726953 0.13060097 0.17937604
## 15 09Tr021 2009 RS 0.16999951 0.027364395 0.11309213 0.22690688
## 16 10Tr001 2010 RS 0.22621668 0.022299355 0.17984264 0.27259073
## 17 10Tr002 2010 RS 0.03229400 0.009516684 0.01250297 0.05208503
## 18 10Tr003 2010 RS 0.39109587 0.028861084 0.33107596 0.45111578
## 19 10Tr042 2010 RS 0.25283916 0.084760435 0.07657018 0.42910813
## 20 10Tr045 2010 RS 0.23207492 0.021314884 0.18774819 0.27640165
## 21 11Tr005 2011 RS 0.63981756 0.028947174 0.57961861 0.70001650
## 22 11Tr006 2011 RS 0.14485110 0.011232820 0.12149117 0.16821103
## 23 11Tr007 2011 RS 0.12785926 0.014061763 0.09861622 0.15710230
## 24 11Tr074 2011 RS 0.83486551 0.069172466 0.69101349 0.97871753
## 25 11Tr075 2011 RS 0.68746196 0.065458744 0.55133305 0.82359087
## 26 UEM-2580 2011 PR 0.38821005 0.071959286 0.23856252 0.53785758
## 27 UEM-2628 2011 PR 0.22961600 0.034565548 0.15773301 0.30149899
## 28 UEM-2665 2011 PR 0.42802281 0.043993249 0.33653385 0.51951178
## 29 UEM-2687 2011 PR 0.43148927 0.060709306 0.30523735 0.55774118
## 30 UEM-2703 2011 PR 0.16773016 0.013936273 0.13874810 0.19671223
## 31 UEM-2705 2011 PR 0.41416778 0.048163026 0.31400729 0.51432828
## 32 3066 2012 PR 0.66847601 0.032328476 0.60124527 0.73570676
## 33 3070 2012 PR 0.12496274 0.042235425 0.03712937 0.21279612
## 34 3071 2012 PR 0.12292312 0.018404328 0.08464923 0.16119701
## 35 3072 2012 PR 0.44949054 0.071440218 0.30092248 0.59805861
## 36 3279 2012 PR 0.50888777 0.039834388 0.42604762 0.59172792
## 37 3178 2012 PR 0.28989704 0.051394338 0.18301666 0.39677741
## 38 UEM-3687 2013 PR 0.22039011 0.018365067 0.18219787 0.25858236
## 39 UEM-3688 2013 PR 0.63878863 0.077013231 0.47863084 0.79894641
## 40 UEM-3696 2013 PR 0.23840711 0.092076947 0.04692262 0.42989161
## 41 UEM-3697 2013 PR 0.61996308 0.092059324 0.42851523 0.81141092
## 42 3393 2013 PR 0.64165091 0.038980033 0.56058749 0.72271433
## 43 3396 2013 PR 0.40536493 0.111277836 0.17395000 0.63677986
## 44 3705 2014 PR 0.23460145 0.085156071 0.05750970 0.41169319
## 45 3710 2014 PR 0.30173519 0.059343556 0.17832351 0.42514687
## 46 3711 2014 PR 0.12608417 0.032137041 0.05925154 0.19291681
## 47 3712 2014 PR 0.18686210 0.024134572 0.13667151 0.23705269
## 48 3713 2014 PR 0.21881656 0.037451567 0.14093176 0.29670136
## 49 3720 2014 PR 0.19968926 0.027838079 0.14179681 0.25758172
## 50 3708 2014 PR 0.17884923 0.020813614 0.13556495 0.22213351Statistics
PARANÁ - PR
ec50_ger %>%
#filter(Population=="PR") %>%
summarise(mean=mean(Estimate),
median=median(Estimate),
sd=sd(Estimate),
lower= quantile(Estimate, 0.25),
upper= quantile(Estimate, 0.75))## mean median sd lower upper
## 1 0.338798 0.2509379 0.220033 0.1877656 0.4449902RIO GRANDE DO SUL- RS
ec50_ger %>%
filter(Population=="RS") %>%
summarise(mean=mean(Estimate),
median=median(Estimate),
sd=sd(Estimate),
lower= quantile(Estimate, 0.25),
upper= quantile(Estimate, 0.75))## mean median sd lower upper
## 1 0.3401929 0.2490367 0.2608178 0.1699995 0.4570033ALL ISOLATES
ec50_ger %>%
filter(Population=="PR") %>%
summarise(mean=mean(Estimate),
median=median(Estimate),
sd=sd(Estimate),
lower= quantile(Estimate, 0.25),
upper= quantile(Estimate, 0.75))## mean median sd lower upper
## 1 0.337403 0.289897 0.1755458 0.1996893 0.4314893##Anova
aov_Ger<- lm(sqrt(Estimate)~Population,data= ec50_ger)
summary(aov_Ger)##
## Call:
## lm(formula = sqrt(Estimate) ~ Population, data = ec50_ger)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37571 -0.12612 -0.04389 0.10501 0.52004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.56204 0.03676 15.288 <2e-16 ***
## PopulationRS -0.01722 0.05199 -0.331 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1838 on 48 degrees of freedom
## Multiple R-squared: 0.002279, Adjusted R-squared: -0.01851
## F-statistic: 0.1097 on 1 and 48 DF, p-value: 0.742Anova(aov_Ger)## Anova Table (Type II tests)
##
## Response: sqrt(Estimate)
## Sum Sq Df F value Pr(>F)
## Population 0.0037 1 0.1097 0.742
## Residuals 1.6218 48Homocedasticity
leveneTest(Estimate~Population,data=ec50_ger)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.5741 0.4523
## 48##Normality
shapiro.test(residuals(aov_Ger))##
## Shapiro-Wilk normality test
##
## data: residuals(aov_Ger)
## W = 0.97012, p-value = 0.2342Boxplot Ec50
box_pyra = ec50_total_pyra=ec50_ger %>%
filter(Estimate<100) %>%
ggplot(aes(Population, log(Estimate)))+
ylim(-6, 6)+
geom_jitter(size=2, color = "black", width = 0.05)+
geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
scale_color_colorblind()+
#geom_hline(yintercept = .50, color="red")+
#annotate("text", x=2.5,y=0.55, label="DD", size=4, color="red")+
#facet_wrap("Population")+
theme_minimal_hgrid()+
labs(y = "log of EC50 ",
x="")
ggsave("img/box_PYRA.png", dpi = 500, height = 3.5, width = 5)
box_pyra
EC50 Azox
Data
This database contains information about all germination tests conducted, divided by: experiment and population.
germination_assay_azox<- gsheet2tbl("https://docs.google.com/spreadsheets/d/1Mis_iDuVrq8x_IpjJDdhAeNPKHiIAZfea9sljgI1sIw/edit#gid=0") %>%
group_by(Isolate, Dose, Replicates, Trial) %>%
dplyr::mutate(GC = as.numeric(GC)) %>%
dplyr::mutate(Dose = as.numeric(Dose))
germination_assay_azox## # A tibble: 700 × 8
## # Groups: Isolate, Dose, Replicates, Trial [700]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT05 07Tr037 2007 1 RS 0 1 45
## 2 FGT05 07Tr037 2007 1 RS 0 2 49
## 3 FGT05 07Tr037 2007 1 RS 0.001 1 42
## 4 FGT05 07Tr037 2007 1 RS 0.001 2 42
## 5 FGT05 07Tr037 2007 1 RS 0.01 1 43
## 6 FGT05 07Tr037 2007 1 RS 0.01 2 40
## 7 FGT05 07Tr037 2007 1 RS 0.1 1 35
## 8 FGT05 07Tr037 2007 1 RS 0.1 2 35
## 9 FGT05 07Tr037 2007 1 RS 1 1 32
## 10 FGT05 07Tr037 2007 1 RS 1 2 30
## # … with 690 more rows##Visualization
germination_assay_azox %>%
ggplot(aes(as.factor(Dose), GC, color=as.factor(Trial)))+
stat_summary(fun.data = "mean_cl_boot",
geom = "errorbar",
width = 0,
size =.5,
position = position_dodge(width=.5))+
stat_summary(fun = "mean",
size = 1.4,
geom = "point",
position = position_dodge(width=.5))+
facet_wrap(~Isolate)+
scale_color_colorblind()+
theme_minimal_hgrid(font_size = 6)+
labs(x = expression(Dose~~μg/ml), y = "Number of germinated conidia/dose", color="")
##Models
Seven isolates were chosen to select the best model that fits all the data set.
Isolates: “07Tr012”
a1<-germination_assay_azox %>%
filter(Isolate=="07Tr012")
a1## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT06 07Tr012 2007 1 RS 0 1 50
## 2 FGT06 07Tr012 2007 1 RS 0 2 49
## 3 FGT06 07Tr012 2007 1 RS 0.001 1 48
## 4 FGT06 07Tr012 2007 1 RS 0.001 2 45
## 5 FGT06 07Tr012 2007 1 RS 0.01 1 45
## 6 FGT06 07Tr012 2007 1 RS 0.01 2 45
## 7 FGT06 07Tr012 2007 1 RS 0.1 1 42
## 8 FGT06 07Tr012 2007 1 RS 0.1 2 41
## 9 FGT06 07Tr012 2007 1 RS 1 1 39
## 10 FGT06 07Tr012 2007 1 RS 1 2 38
## # … with 18 more rowsaz1=drm(GC~Dose, data=a1, fct = W2.3())
plot(az1)
summary(az1)##
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) -0.192048 0.040655 -4.7238 7.623e-05 ***
## d:(Intercept) 49.264647 2.717077 18.1315 6.702e-16 ***
## e:(Intercept) 1.157380 0.923278 1.2536 0.2216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 6.599493 (25 degrees of freedom)ED(az1, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 7.8037 5.6314mselect(az1,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]## logLik IC Lack of fit Res var
## MM.3 -90.85473 189.7095 8.707204e-01 43.16696
## W1.3u -90.95708 189.9142 8.433111e-01 43.48369
## W2.3 -90.97948 189.9590 8.371752e-01 43.55330
## W2.3 -90.97948 189.9590 8.371752e-01 43.55330
## AR.3 -91.35789 190.7158 7.295222e-01 44.74660
## LL2.3u -91.45164 190.9033 7.025153e-01 45.04724
## LL.3u -91.45164 190.9033 7.025151e-01 45.04724
## LL2.4 -90.47308 190.9462 8.892328e-01 43.75633
## LL.4 -90.47308 190.9462 8.892328e-01 43.75633
## LL2.3 -91.49766 190.9953 6.893097e-01 45.19557
## LL.3 -91.49766 190.9953 6.893097e-01 45.19557
## W1.4 -90.60163 191.2033 8.426163e-01 44.15996
## W2.4 -90.64875 191.2975 8.252081e-01 44.30883
## W1.3 -92.14782 192.2956 5.132524e-01 47.34394
## BC.5 -90.42954 192.8591 7.589586e-01 45.51701
## LL.5 -90.47147 192.9429 7.354639e-01 45.65353
## LL2.5 -90.47147 192.9429 7.354632e-01 45.65353
## W1.2 -141.39270 288.7854 5.574980e-16 1534.11538
## LL2.2 -141.39270 288.7854 5.574980e-16 1534.11538
## LL.2 -141.44315 288.8863 5.368665e-16 1539.65379
## W2.2 -141.50665 289.0133 5.119780e-16 1546.65374
## AR.2 NA NA NA NA
## MM.2 NA NA NA NA“09Tr019”
a2<-germination_assay_azox %>%
filter(Isolate=="09Tr019")
a2## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT73 09Tr019 2009 1 RS 0 1 40
## 2 FGT73 09Tr019 2009 1 RS 0 2 39
## 3 FGT73 09Tr019 2009 1 RS 0.001 1 33
## 4 FGT73 09Tr019 2009 1 RS 0.001 2 38
## 5 FGT73 09Tr019 2009 1 RS 0.01 1 33
## 6 FGT73 09Tr019 2009 1 RS 0.01 2 34
## 7 FGT73 09Tr019 2009 1 RS 0.1 1 33
## 8 FGT73 09Tr019 2009 1 RS 0.1 2 30
## 9 FGT73 09Tr019 2009 1 RS 1 1 25
## 10 FGT73 09Tr019 2009 1 RS 1 2 29
## # … with 18 more rowsaz2=drm(GC~Dose, data=a2, fct = W1.3())
plot(az2)
ggsave("img/az2.png", dpi = 500)## Saving 7 x 5 in imagesummary(az2)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.289553 0.074216 3.9015 0.0006378 ***
## d:(Intercept) 44.723739 2.627931 17.0186 2.908e-15 ***
## e:(Intercept) 49.881337 27.458284 1.8166 0.0812845 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 6.515775 (25 degrees of freedom)ED(az2, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 14.067 8.234mselect(az2,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]## logLik IC Lack of fit Res var
## W1.3 -90.62201 189.2440 9.992104e-01 42.45533
## W1.3 -90.62201 189.2440 9.992104e-01 42.45533
## LL2.3 -90.63175 189.2635 9.988879e-01 42.48487
## LL.3 -90.63175 189.2635 9.988879e-01 42.48487
## LL2.3u -90.63971 189.2794 9.985847e-01 42.50904
## LL.3u -90.63975 189.2795 9.985833e-01 42.50915
## W2.3 -90.98444 189.9689 9.574162e-01 43.56876
## W1.4 -90.59981 191.1996 9.975205e-01 44.15423
## LL2.4 -90.61070 191.2214 9.960268e-01 44.18858
## LL.4 -90.62179 191.2436 9.942947e-01 44.22359
## W2.4 -90.75671 191.5134 9.627263e-01 44.65186
## MM.3 -92.09497 192.1899 6.641055e-01 47.16555
## BC.5 -90.60529 193.2106 9.743879e-01 46.09202
## LL2.5 -90.60952 193.2190 9.713044e-01 46.10593
## LL.5 -90.62001 193.2400 9.636933e-01 46.14049
## AR.3 -93.43789 194.8758 3.428513e-01 51.91389
## LL.2 -139.23997 284.4799 4.073089e-15 1315.46154
## W1.2 -139.23997 284.4799 4.073089e-15 1315.46154
## LL2.2 -139.23997 284.4799 4.073089e-15 1315.46154
## W2.2 -139.35655 284.7131 3.733426e-15 1326.46152
## W1.3u NA NA NA NA
## AR.2 NA NA NA NA
## MM.2 NA NA NA NA“11Tr074”
a3<-germination_assay_azox %>%
filter(Isolate=="11Tr074")
a3## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT133 11Tr074 2011 1 RS 0 1 43
## 2 FGT133 11Tr074 2011 1 RS 0 2 47
## 3 FGT133 11Tr074 2011 1 RS 0.001 1 22
## 4 FGT133 11Tr074 2011 1 RS 0.001 2 20
## 5 FGT133 11Tr074 2011 1 RS 0.01 1 16
## 6 FGT133 11Tr074 2011 1 RS 0.01 2 22
## 7 FGT133 11Tr074 2011 1 RS 0.1 1 20
## 8 FGT133 11Tr074 2011 1 RS 0.1 2 17
## 9 FGT133 11Tr074 2011 1 RS 1 1 16
## 10 FGT133 11Tr074 2011 1 RS 1 2 11
## # … with 18 more rowsaz3=drm(GC~Dose, data=a3, fct = W1.3())
plot(az3)
summary(az3)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.156492 0.046572 3.3602 0.002502 **
## d:(Intercept) 46.513758 4.692831 9.9117 3.834e-10 ***
## e:(Intercept) 3.445858 3.962541 0.8696 0.392788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 9.332128 (25 degrees of freedom)ED(az3, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.33125 0.46341mselect(az3,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -100.6806 209.3613 9.635540e-01 87.08862
## W1.3 -100.6806 209.3613 9.635540e-01 87.08862
## LL2.3 -100.9568 209.9137 9.053661e-01 88.82370
## LL.3 -100.9568 209.9137 9.053654e-01 88.82372
## W2.3 -101.3578 210.7156 7.982989e-01 91.40455
## W1.3u -101.4084 210.8169 7.838771e-01 91.73563
## LL2.4 -100.6044 211.2088 9.264313e-01 90.22454
## W1.4 -100.6131 211.2262 9.235396e-01 90.28064
## LL.4 -100.7269 211.4539 8.840873e-01 91.01785
## W2.4 -100.8201 211.6402 8.503173e-01 91.62544
## LL2.5 -100.6033 213.2067 7.972422e-01 94.14028
## LL.5 -100.6375 213.2749 7.770969e-01 94.37002
## BC.5 -100.6766 213.3532 7.546205e-01 94.63418
## LL2.3u -127.9633 263.9266 9.813056e-09 611.36000
## LL2.2 -134.1330 274.2659 2.547511e-10 913.38462
## W1.2 -134.1695 274.3389 2.479572e-10 915.76923
## LL.2 -134.2270 274.4540 2.376174e-10 919.53928
## W2.2 -134.3569 274.7139 2.158145e-10 928.11426
## LL.3u NA NA NA NA
## AR.2 NA NA NA NA
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“3070”
a4<-germination_assay_azox %>%
filter(Isolate=="3070")
a4## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT187 3070 2012 1 PR 0 1 25
## 2 FGT187 3070 2012 1 PR 0 2 21
## 3 FGT187 3070 2012 1 PR 0.001 1 18
## 4 FGT187 3070 2012 1 PR 0.001 2 17
## 5 FGT187 3070 2012 1 PR 0.01 1 16
## 6 FGT187 3070 2012 1 PR 0.01 2 15
## 7 FGT187 3070 2012 1 PR 0.1 1 14
## 8 FGT187 3070 2012 1 PR 0.1 2 12
## 9 FGT187 3070 2012 1 PR 1 1 5
## 10 FGT187 3070 2012 1 PR 1 2 5
## # … with 18 more rowsaz4=drm(GC~Dose, data=a4, fct = W1.3())
plot(az4)
summary(az4)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.62237 0.39968 1.5572 0.1320
## d:(Intercept) 32.64611 4.10244 7.9577 2.588e-08 ***
## e:(Intercept) 0.46747 0.29805 1.5684 0.1293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 9.917212 (25 degrees of freedom)ED(az4, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.25942 0.20474mselect(az4,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -102.3833 212.7666 9.832680e-01 98.35109
## W1.3 -102.3833 212.7666 9.832680e-01 98.35109
## LL2.3 -102.5096 213.0193 9.643266e-01 99.24274
## LL.3 -102.5096 213.0193 9.643252e-01 99.24280
## W2.3 -102.6285 213.2571 9.417458e-01 100.08921
## W1.4 -102.3826 214.7653 9.442207e-01 102.44423
## LL.4 -102.5080 215.0160 9.025729e-01 103.36586
## LL2.4 -102.5096 215.0192 9.020234e-01 103.37751
## W2.4 -102.6188 215.2375 8.630400e-01 104.18676
## LL2.5 -102.3897 216.7795 8.258859e-01 106.95266
## LL.5 -102.4473 216.8947 7.909700e-01 107.39356
## BC.5 -102.4986 216.9972 7.611464e-01 107.78739
## W1.2 -128.1994 262.3989 7.792768e-08 597.84615
## LL2.2 -128.2066 262.4133 7.751768e-08 598.15385
## LL.2 -128.2389 262.4777 7.571032e-08 599.53180
## W2.2 -128.2390 262.4780 7.570212e-08 599.53813
## W1.3u -128.1994 264.3989 3.194539e-08 621.76000
## LL.3u -128.1994 264.3989 3.194539e-08 621.76000
## LL2.3u -128.2066 264.4133 3.177596e-08 622.08000
## AR.2 NA NA NA NA
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NA“UEM-3688”
a5<-germination_assay_azox %>%
filter(Isolate=="UEM-3688")
a5## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT202 UEM-3688 2013 1 PR 0 1 49
## 2 FGT202 UEM-3688 2013 1 PR 0 2 50
## 3 FGT202 UEM-3688 2013 1 PR 0.001 1 48
## 4 FGT202 UEM-3688 2013 1 PR 0.001 2 47
## 5 FGT202 UEM-3688 2013 1 PR 0.01 1 47
## 6 FGT202 UEM-3688 2013 1 PR 0.01 2 45
## 7 FGT202 UEM-3688 2013 1 PR 0.1 1 46
## 8 FGT202 UEM-3688 2013 1 PR 0.1 2 43
## 9 FGT202 UEM-3688 2013 1 PR 1 1 43
## 10 FGT202 UEM-3688 2013 1 PR 1 2 40
## # … with 18 more rowsaz5=drm(GC~Dose, data=a5, fct = W2.3())
plot(az5)
summary(az5)##
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) -0.087403 0.027825 -3.1412 0.00429 **
## d:(Intercept) 47.732940 3.335571 14.3103 1.512e-13 ***
## e:(Intercept) 10.730341 19.075306 0.5625 0.57877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 7.187007 (25 degrees of freedom)ED(az5, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 710.85 1585.98mselect(az5,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]## logLik IC Lack of fit Res var
## W1.3u -93.35943 194.7189 9.609340e-01 51.62378
## W2.3 -93.36737 194.7347 9.595087e-01 51.65307
## W2.3 -93.36737 194.7347 9.595087e-01 51.65307
## LL2.3u -93.44986 194.8997 9.436143e-01 51.95831
## LL2.3 -93.45188 194.9038 9.432018e-01 51.96580
## W1.3 -93.50351 195.0070 9.323043e-01 52.15781
## LL.3u -94.18050 196.3610 7.528002e-01 54.74195
## LL.3 -94.22697 196.4539 7.393993e-01 54.92393
## W2.4 -93.34562 196.6912 9.000683e-01 53.72174
## LL2.4 -93.40246 196.8049 8.799291e-01 53.94029
## LL.4 -93.40286 196.8057 8.797840e-01 53.94185
## W1.4 -93.43907 196.8781 8.667163e-01 54.08153
## MM.3 -94.75360 197.5072 5.907549e-01 57.02936
## AR.3 -95.07657 198.1531 5.069220e-01 58.36026
## BC.5 -93.09283 198.1857 9.086504e-01 55.05434
## LL2.5 -93.29833 198.5967 7.788620e-01 55.86843
## LL.5 -93.39543 198.7909 7.241544e-01 56.25729
## W1.2 -140.97806 287.9561 6.649486e-15 1489.34615
## LL2.2 -140.97806 287.9561 6.649486e-15 1489.34615
## LL.2 -141.02113 288.0423 6.439025e-15 1493.93513
## W2.2 -141.19622 288.3924 5.649864e-15 1512.73545
## AR.2 NA NA NA NA
## MM.2 NA NA NA NA“UEM-2580”
a6<-germination_assay_azox %>%
filter(Isolate=="UEM-2580")
a6## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT152 UEM-2580 2011 1 PR 0 1 45
## 2 FGT152 UEM-2580 2011 1 PR 0 2 44
## 3 FGT152 UEM-2580 2011 1 PR 0.001 1 42
## 4 FGT152 UEM-2580 2011 1 PR 0.001 2 43
## 5 FGT152 UEM-2580 2011 1 PR 0.01 1 40
## 6 FGT152 UEM-2580 2011 1 PR 0.01 2 42
## 7 FGT152 UEM-2580 2011 1 PR 0.1 1 37
## 8 FGT152 UEM-2580 2011 1 PR 0.1 2 37
## 9 FGT152 UEM-2580 2011 1 PR 1 1 21
## 10 FGT152 UEM-2580 2011 1 PR 1 2 27
## # … with 18 more rowsaz6=drm(GC~Dose, data=a6, fct = W2.3())
plot(az6)
summary(az6)##
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) -0.27977 0.04879 -5.7342 5.655e-06 ***
## d:(Intercept) 46.11386 1.62593 28.3616 < 2.2e-16 ***
## e:(Intercept) 1.92918 0.86654 2.2263 0.03524 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 5.189595 (25 degrees of freedom)ED(az6, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 7.1501 2.9768mselect(az6,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]## logLik IC Lack of fit Res var
## MM.3 -84.00091 176.0018 9.137416e-01 26.45689
## W1.3u -84.21081 176.4216 8.608374e-01 26.85654
## W2.3 -84.25004 176.5001 8.503025e-01 26.93189
## W2.3 -84.25004 176.5001 8.503025e-01 26.93189
## W1.4 -83.48507 176.9701 9.836894e-01 26.56231
## LL2.4 -83.61199 177.2240 9.492979e-01 26.80420
## LL.4 -83.61199 177.2240 9.492979e-01 26.80420
## LL2.3u -84.63713 177.2743 7.409016e-01 27.68695
## LL.3u -84.63713 177.2743 7.409012e-01 27.68695
## LL2.3 -84.72136 177.4427 7.166056e-01 27.85403
## LL.3 -84.72136 177.4427 7.166054e-01 27.85403
## W2.4 -83.98056 177.9611 8.203410e-01 27.51922
## AR.3 -84.98880 177.9776 6.404033e-01 28.39123
## BC.5 -83.47761 178.9552 9.297348e-01 27.70242
## LL2.5 -83.48953 178.9791 9.214647e-01 27.72601
## LL.5 -83.52837 179.0567 8.950082e-01 27.80304
## W1.3 -85.71767 179.4353 4.529994e-01 29.90848
## W1.2 -140.56493 287.1299 6.997071e-18 1446.03846
## LL2.2 -140.56493 287.1299 6.997071e-18 1446.03846
## LL.2 -140.60657 287.2131 6.782383e-18 1450.34616
## W2.2 -140.66289 287.3258 6.502481e-18 1456.19223
## AR.2 NA NA NA NA
## MM.2 NA NA NA NA“3711”
a7<-germination_assay_azox %>%
filter(Isolate=="3711")
a7## # A tibble: 28 × 8
## # Groups: Isolate, Dose, Replicates, Trial [28]
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 FGT232 3711 2014 1 PR 0 1 48
## 2 FGT232 3711 2014 1 PR 0 2 48
## 3 FGT232 3711 2014 1 PR 0.001 1 47
## 4 FGT232 3711 2014 1 PR 0.001 2 46
## 5 FGT232 3711 2014 1 PR 0.01 1 45
## 6 FGT232 3711 2014 1 PR 0.01 2 44
## 7 FGT232 3711 2014 1 PR 0.1 1 44
## 8 FGT232 3711 2014 1 PR 0.1 2 42
## 9 FGT232 3711 2014 1 PR 1 1 15
## 10 FGT232 3711 2014 1 PR 1 2 9
## # … with 18 more rowsaz7=drm(GC~Dose, data=a7, fct = W2.3())
plot(az7)
summary(az7)##
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) -0.855739 0.199653 -4.2861 0.0002369 ***
## d:(Intercept) 40.844941 1.804982 22.6290 < 2.2e-16 ***
## e:(Intercept) 0.195578 0.053832 3.6331 0.0012626 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 6.258056 (25 degrees of freedom)ED(az7, 50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.30014 0.08533mselect(az7,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))## Warning in sqrt(diag(varMat)): NaNs produced
## Warning in sqrt(diag(varMat)): NaNs produced## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
## non-finite finite-difference value [3]## logLik IC Lack of fit Res var
## W1.3 -89.22218 186.4444 6.984540e-01 38.41562
## LL.3 -89.33711 186.6742 6.656245e-01 38.73230
## LL2.3 -89.33711 186.6742 6.656245e-01 38.73230
## W2.3 -89.49203 186.9841 6.221527e-01 39.16327
## W2.3 -89.49203 186.9841 6.221527e-01 39.16327
## W1.4 -88.98268 187.9654 6.177169e-01 39.33755
## LL.4 -89.23166 188.4633 5.380240e-01 40.04339
## LL2.4 -89.33729 188.6746 5.065404e-01 40.34665
## W2.4 -89.42312 188.8462 4.820043e-01 40.59478
## LL2.5 -88.99636 189.9927 4.132803e-01 41.08801
## LL.5 -89.04649 190.0930 3.980330e-01 41.23537
## BC.5 -89.19959 190.3992 3.548527e-01 41.68881
## LL.3u -117.68010 243.3602 1.924544e-09 293.29333
## LL2.2 -134.63384 275.2677 1.625564e-14 946.65385
## W1.2 -134.63725 275.2745 1.621429e-14 946.88462
## LL.2 -134.64296 275.2859 1.614535e-14 947.27081
## W2.2 -134.68040 275.3608 1.570038e-14 949.80747
## W1.3u -134.63369 277.2674 6.276033e-15 984.51000
## LL2.3u -134.63384 277.2677 6.275366e-15 984.52000
## AR.2 NA NA NA NA
## AR.3 NA NA NA NA
## MM.2 NA NA NA NA
## MM.3 NA NA NA NAEC50 Azox
Calculation of Ec50 for the number of germinated conidia in relation to the dose, being stratified by year and population (RS, PR). The model was chosen through the tests performed above considering <IAC and best curve fit.
Model: W1.3
ec50_ger_AZX=estimate_EC50(GC~Dose,
data= germination_assay_azox ,
isolate_col= "Isolate",
strata_col = c("Year", "Population"),
fct=drc::W1.3(),
interval="delta")
ec50_ger_AZX## ID Year Population Estimate Std..Error Lower Upper
## 1 07Tr037 2007 RS 15.3972660 6.11655980 2.799975e+00 27.9945568
## 2 07Tr012 2007 RS 8.8559430 6.13352254 -3.776283e+00 21.4881692
## 3 07Tr013 2007 RS 0.3346700 0.19531041 -6.757936e-02 0.7369193
## 4 08Tr003 2008 RS 1.0944292 0.53585583 -9.186549e-03 2.1980449
## 5 08Tr013 2008 RS 2.9323113 1.63145054 -4.277240e-01 6.2923466
## 6 09Tr005 2009 RS 9.9565971 2.62264709 4.555154e+00 15.3580399
## 7 09Tr019 2009 RS 14.0673655 8.23396078 -2.890794e+00 31.0255252
## 8 10Tr002 2010 RS 9.9872003 4.27385204 1.185037e+00 18.7893634
## 9 10Tr003 2010 RS 0.5540930 0.22228724 9.628388e-02 1.0119022
## 10 11Tr005 2011 RS 277.9861111 184.72403078 -1.024602e+02 658.4323741
## 11 11Tr007 2011 RS 0.5677651 0.19580669 1.644936e-01 0.9710365
## 12 11Tr074 2011 RS 0.3312518 0.46340539 -6.231494e-01 1.2856531
## 13 11Tr075 2011 RS 4.0419176 2.58939046 -1.291032e+00 9.3748670
## 14 UEM-2580 2011 PR 8.2703330 3.74579346 5.557270e-01 15.9849391
## 15 UEM-2703 2011 PR 0.6131040 0.15327277 2.974329e-01 0.9287752
## 16 UEM-2705 2011 PR 2.5306268 0.53896788 1.420602e+00 3.6406519
## 17 3066 2012 PR 3.2252221 1.75083370 -3.806874e-01 6.8311316
## 18 3070 2012 PR 0.2594182 0.20473648 -1.622445e-01 0.6810808
## 19 3072 2012 PR 11.7019076 6.01149485 -6.789978e-01 24.0828130
## 20 UEM-3687 2013 PR 5.0744625 2.15384647 6.385326e-01 9.5103923
## 21 UEM-3688 2013 PR 268.0577834 394.45802381 -5.443437e+02 1080.4592909
## 22 3396 2013 PR 0.5257408 0.09866538 3.225357e-01 0.7289460
## 23 3705 2014 PR 0.7588132 0.27567321 1.910536e-01 1.3265728
## 24 3710 2014 PR 1.8818038 0.39278461 1.072849e+00 2.6907589
## 25 3711 2014 PR 0.3724231 0.12145307 1.222858e-01 0.6225604Statistics
PARANÁ - PR
ec50_ger_AZX %>%
filter(Population=="PR") %>%
summarise(mean=mean(Estimate),
median=median(Estimate),
sd=sd(Estimate),
lower= quantile(Estimate, 0.25),
upper= quantile(Estimate, 0.75))## mean median sd lower upper
## 1 25.27264 2.206215 76.54076 0.5912632 5.87343RIO GRANDE DO SUL - RS
ec50_ger_AZX %>%
filter(Population=="RS") %>%
summarise(mean=mean(Estimate),
median=median(Estimate),
sd=sd(Estimate),
lower= quantile(Estimate, 0.25),
upper= quantile(Estimate, 0.75))## mean median sd lower upper
## 1 26.62361 4.041918 75.71928 0.5677651 9.9872ALL ISOLATES
ec50_ger_AZX %>%
summarise(mean=mean(Estimate),
median=median(Estimate),
sd=sd(Estimate),
lower_azox= quantile(Estimate, 0.25),
upper_azox= quantile(Estimate, 0.75))## mean median sd lower_azox upper_azox
## 1 25.97514 2.932311 74.51389 0.5677651 9.956597##Anova
aov_Ger_azox<- lm((Estimate)~Population,data= ec50_ger_AZX)
summary(aov_Ger_azox)##
## Call:
## lm(formula = (Estimate) ~ Population, data = ec50_ger_AZX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.29 -24.90 -22.74 -16.67 251.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.273 21.972 1.150 0.262
## PopulationRS 1.351 30.470 0.044 0.965
##
## Residual standard error: 76.11 on 23 degrees of freedom
## Multiple R-squared: 8.547e-05, Adjusted R-squared: -0.04339
## F-statistic: 0.001966 on 1 and 23 DF, p-value: 0.965Anova(aov_Ger_azox)## Anova Table (Type II tests)
##
## Response: (Estimate)
## Sum Sq Df F value Pr(>F)
## Population 11 1 0.002 0.965
## Residuals 133244 23Homocedasticity
leveneTest(Estimate~Population,data=ec50_ger_AZX)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 9e-04 0.977
## 23##Normality
shapiro.test(residuals(aov_Ger_azox))##
## Shapiro-Wilk normality test
##
## data: residuals(aov_Ger_azox)
## W = 0.36076, p-value = 2.033e-09GLM
glm_azox = glm(Estimate ~ Population , family = Gamma (link=log), data = ec50_ger_AZX )
summary(glm_azox)##
## Call:
## glm(formula = Estimate ~ Population, family = Gamma(link = log),
## data = ec50_ger_AZX)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6793 -2.3955 -1.6224 -0.8456 3.8066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.22972 0.84691 3.814 0.000893 ***
## PopulationRS 0.05208 1.17445 0.044 0.965015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 8.60701)
##
## Null deviance: 107.32 on 24 degrees of freedom
## Residual deviance: 107.31 on 23 degrees of freedom
## AIC: 183.28
##
## Number of Fisher Scoring iterations: 13##Boxplot Ec50
box_azox_2=ec50_total_azox=ec50_ger_AZX %>%
ggplot(aes(Population, log(Estimate)))+
geom_jitter(size=2, color = "black", width = 0.05)+
ylim(-6,6)+
geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
scale_color_colorblind()+
#geom_hline(yintercept = 5, color="red")+
#annotate("text", x=2.5,y=5.5, label="DD", size=4, color="red")+
#facet_wrap("Population")+
theme_minimal_hgrid()+
labs(y = "Log of EC50",
x="")
ggsave("img/box_AZOX_2.png", dpi = 500, height = 3.5, width = 5)
box_azox_2
EC50 (both fungicides)
Here we loaded in the same data frame the EC50 values for both fungicides.
ec50_fun= gsheet2tbl("https://docs.google.com/spreadsheets/d/116vNPmNBOEoH0oWIFZxV9B8gkkv-wcYJ_fhbW2rpBZU/edit#gid=1135537345")
ec50_fun## # A tibble: 50 × 7
## `Work Code` Isolate State City Year Fungicide Estimate
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl>
## 1 FGT05 07Tr037 RS Cruz Alta 2007 Pyraclostrobin 0.0286
## 2 FGT06 07Tr012 RS Cruz Alta 2007 Pyraclostrobin 0.513
## 3 FGT07 07Tr013 RS Cruz Alta 2007 Pyraclostrobin 1.13
## 4 FGT33 08Tr003 RS Santa Rosa 2008 Pyraclostrobin 0.235
## 5 FGT43 08Tr013 RS Giruá 2008 Pyraclostrobin 0.462
## 6 FGT62 09Tr005 RS Panambi 2009 Pyraclostrobin 0.365
## 7 FGT73 09Tr019 RS Cruz Alta 2009 Pyraclostrobin 0.130
## 8 FGT92 10Tr002 RS Passo Fundo 2010 Pyraclostrobin 0.0323
## 9 FGT93 10Tr003 RS Passo Fundo 2010 Pyraclostrobin 0.391
## 10 FGT125 11Tr005 RS Ijuí 2011 Pyraclostrobin 0.640
## # … with 40 more rowsBoxplot (azox and pyra)
box_azox_pyra <- ec50_fun %>%
filter(Estimate<100) %>%
ggplot(aes(Fungicide, log(Estimate)))+
geom_boxplot(width=0.5, outlier.color = NA, size =1)+
ylim(-6, 6)+
geom_jitter(size=2, color = "black",fill="black", width = 0.05)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank())+
theme_minimal_hgrid()+
scale_fill_few()+
labs(x = "Fungicides",
y= "Log of EC50")+
theme(legend.position = "none")Figure 2
(box_azox_2 | box_pyra)+
plot_annotation(tag_levels = 'A')
ggsave("img/figure2.png", width = 8, height =5)Correlation EC50
cor_ec50= gsheet2tbl("https://docs.google.com/spreadsheets/d/116vNPmNBOEoH0oWIFZxV9B8gkkv-wcYJ_fhbW2rpBZU/edit#gid=0") %>%
mutate(pyra=log(pyra)) %>%
mutate(azox=log(azox))
cor_ec50## # A tibble: 25 × 7
## `Work Code` Isolate State City Year pyra azox
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 FGT05 07Tr037 RS Cruz Alta 2007 -3.55 2.73
## 2 FGT06 07Tr012 RS Cruz Alta 2007 -0.668 2.18
## 3 FGT07 07Tr013 RS Cruz Alta 2007 0.126 -1.09
## 4 FGT33 08Tr003 RS Santa Rosa 2008 -1.45 0.0902
## 5 FGT43 08Tr013 RS Giruá 2008 -0.772 1.08
## 6 FGT62 09Tr005 RS Panambi 2009 -1.01 2.30
## 7 FGT73 09Tr019 RS Cruz Alta 2009 -2.04 2.64
## 8 FGT92 10Tr002 RS Passo Fundo 2010 -3.43 2.30
## 9 FGT93 10Tr003 RS Passo Fundo 2010 -0.939 -0.590
## 10 FGT125 11Tr005 RS Ijuí 2011 -0.447 5.80
## # … with 15 more rowscor.test((cor_ec50$azox), (cor_ec50$pyra), method ="spearman")##
## Spearman's rank correlation rho
##
## data: (cor_ec50$azox) and (cor_ec50$pyra)
## S = 2552, p-value = 0.9311
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.01846154cor_ec50 %>%
ggplot(aes(pyra, azox))+
#geom_smooth(method=lm)+
geom_point(shape=21, size=3)+
theme_minimal()+
labs(x =expression((Log)~EC[50]~~μg/ml~Pyraclostrobin), y = expression((Log)~EC[50]~~μg/ml~Azoxystrobin, color=""))
ggsave("img/cor_ec50_log.png", dpi = 500)## Saving 7 x 5 in imageISOLATE CML3066
We also calculated the EC50 for the isolates CML3066, which is a isolate from Brazil that has its genome sequenced.
EC50_P<- gsheet2tbl("https://docs.google.com/spreadsheets/d/1Mis_iDuVrq8x_IpjJDdhAeNPKHiIAZfea9sljgI1sIw/edit#gid=1606966266") %>%
filter(Code =="GEN-P") %>%
dplyr::mutate(GC = as.numeric(GC)) %>%
dplyr::mutate(Dose = as.numeric(Dose))
EC50_P## # A tibble: 24 × 8
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 GEN-P 09Tr125 2009 1 RS 0 1 45
## 2 GEN-P 09Tr125 2009 1 RS 0 2 37
## 3 GEN-P 09Tr125 2009 1 RS 0.05 1 30
## 4 GEN-P 09Tr125 2009 1 RS 0.05 2 30
## 5 GEN-P 09Tr125 2009 1 RS 0.5 1 16
## 6 GEN-P 09Tr125 2009 1 RS 0.5 2 5
## 7 GEN-P 09Tr125 2009 1 RS 1 1 9
## 8 GEN-P 09Tr125 2009 1 RS 1 2 2
## 9 GEN-P 09Tr125 2009 1 RS 5 1 0
## 10 GEN-P 09Tr125 2009 1 RS 5 2 0
## # … with 14 more rowsEC50 CML3066 Pyra
model_p=drm(GC~Dose, data= EC50_P, fct = W1.3())
plot(model_p)
summary(model_p)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 0.635099 0.068349 9.2920 6.898e-09 ***
## d:(Intercept) 42.650786 1.456686 29.2793 < 2.2e-16 ***
## e:(Intercept) 0.250929 0.034610 7.2503 3.842e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 2.905047 (21 degrees of freedom)ED(model_p,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.140903 0.024111EC50- CML3066 Azox
EC50_AZ<- gsheet2tbl("https://docs.google.com/spreadsheets/d/1Mis_iDuVrq8x_IpjJDdhAeNPKHiIAZfea9sljgI1sIw/edit#gid=1606966266") %>%
filter(Code =="GEN") %>%
dplyr::mutate(GC = as.numeric(GC)) %>%
dplyr::mutate(Dose = as.numeric(Dose))
EC50_AZ## # A tibble: 28 × 8
## Code Isolate Year Trial Population Dose Replicates GC
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 GEN 09Tr125 2009 1 RS 0 1 47
## 2 GEN 09Tr125 2009 1 RS 0 2 46
## 3 GEN 09Tr125 2009 1 RS 0.001 1 42
## 4 GEN 09Tr125 2009 1 RS 0.001 2 40
## 5 GEN 09Tr125 2009 1 RS 0.01 1 42
## 6 GEN 09Tr125 2009 1 RS 0.01 2 40
## 7 GEN 09Tr125 2009 1 RS 0.1 1 39
## 8 GEN 09Tr125 2009 1 RS 0.1 2 38
## 9 GEN 09Tr125 2009 1 RS 1 1 8
## 10 GEN 09Tr125 2009 1 RS 1 2 4
## # … with 18 more rowsEC50 CML3066 Azox
model_az=drm(GC~Dose, data= EC50_AZ, fct = W1.3())
plot(model_az)
summary(model_az)##
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## b:(Intercept) 1.031714 0.154052 6.6972 5.108e-07 ***
## d:(Intercept) 41.616353 1.062145 39.1814 < 2.2e-16 ***
## e:(Intercept) 0.480825 0.066215 7.2615 1.311e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error:
##
## 3.305414 (25 degrees of freedom)ED(model_az,50)##
## Estimated effective doses
##
## Estimate Std. Error
## e:1:50 0.33706 0.05393Discriminatory Dose
We also screening isolates through discriminatory dose for each fungicide.
Load data RS
data_load_rs = gsheet2tbl("https://docs.google.com/spreadsheets/d/1xaHgZCiO85EwuowMlGKlvO1tlwG2SleaBG-L-5x4Enw/edit#gid=0") %>%
mutate(Fungicide = factor(Fungicide, levels = c("Control", "Azoxtrobin","Pyraclostrobin" )))
data_load_rs## # A tibble: 1,890 × 12
## Isolate `Original Code` City lat long Year State Rpetition Trial
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 1 1
## 2 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 2 1
## 3 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 3 1
## 4 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 1 1
## 5 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 2 1
## 6 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 3 1
## 7 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 1 1
## 8 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 2 1
## 9 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 3 1
## 10 1 07Tr003 Cruz Alta -28.6 -53.6 2007 RS 1 2
## # … with 1,880 more rows, and 3 more variables: Fungicide <fct>, Dose <dbl>,
## # conidia <dbl>visualization
data_load_rs %>%
ggplot(aes(Fungicide,conidia))+
geom_jitter()
## Single isolate
single_rs = data_load_rs %>%
filter(Isolate == 1)
single_rs %>%
ggplot(aes(Fungicide,conidia, color = factor(Trial)))+
geom_jitter(height = 0)
Glm
glm_single = glm(conidia ~ Fungicide , family = poisson, data = single_rs)
summary(glm_single)##
## Call:
## glm(formula = conidia ~ Fungicide, family = poisson, data = single_rs)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.96656 -0.16965 0.02360 0.07143 1.04939
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.90868 0.05783 67.587 < 2e-16 ***
## FungicideAzoxtrobin -1.15715 0.11825 -9.786 < 2e-16 ***
## FungicidePyraclostrobin -0.83291 0.10506 -7.928 2.22e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 134.7694 on 17 degrees of freedom
## Residual deviance: 4.5796 on 15 degrees of freedom
## AIC: 102.07
##
## Number of Fisher Scoring iterations: 4coef(glm_single)[2:3]## FungicideAzoxtrobin FungicidePyraclostrobin
## -1.1571488 -0.8329091confint(glm_single)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 3.793151 4.0199272
## FungicideAzoxtrobin -1.393878 -0.9297843
## FungicidePyraclostrobin -1.041798 -0.6296018azox_single = c(exp(coef(glm_single)[2]), exp(confint(glm_single)[2,]))## Waiting for profiling to be done...pyra_single = c(exp(coef(glm_single)[3]), exp(confint(glm_single)[3,]))## Waiting for profiling to be done...Data frame (GLM results)
isolates = unique(data_load_rs$Isolate)
azox_all = NULL
pyra_all = NULL
for(i in 1:length(isolates)){
single_df = data_load_rs %>%
filter(Isolate == isolates[i])
glm_single = glm(conidia ~ Fungicide , family = poisson, data = single_df)
summary(glm_single)
coef(glm_single)[2:3]
confint(glm_single)
azox_single = c(mean = as.numeric(exp(coef(glm_single)[2])), exp(confint(glm_single)[2,]), isol = isolates[i], year = unique(single_df$Year), local = unique(single_df$City),lat = unique(single_df$lat), long = unique(single_df$long), state= (single_df$State))
pyra_single = c(mean = as.numeric(exp(coef(glm_single)[3])), exp(confint(glm_single)[3,]), isol = isolates[i], year = unique(single_df$Year),local = unique(single_df$City), lat = unique(single_df$lat), long = unique(single_df$long), state= (single_df$State))
azox_all = rbind(azox_all, azox_single)
pyra_all = rbind(pyra_all, pyra_single)
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## Waiting for profiling to be done...results_rs = bind_rows(
as.data.frame(azox_all) %>%
mutate(fungicide = "Azoxystrobin"),
as.data.frame(pyra_all) %>%
mutate(fungicide = "Pyraclostrobin")
)Data frame reorganization
results_rs_1<-results_rs %>%
summarise(mean=as.numeric(mean),
lower=as.numeric(`2.5 %`),
upper= as.numeric(`97.5 %`),
lat=as.numeric(lat),
long=as.numeric(long),
isolate= as.numeric(isol),
fungicide=fungicide,
year=year,
state=state1)
results_rs_1## mean lower upper lat long isolate
## 1 0.314381271 0.248111164 0.39463883 -28.63940 -53.60620 1
## 2 0.259385666 0.200213323 0.33186732 -28.63940 -53.60620 2
## 3 0.290657440 0.226658790 0.36870679 -28.63940 -53.60620 3
## 4 0.089965398 0.058789031 0.13161526 -28.63940 -53.60620 4
## 5 0.347826087 0.277078704 0.43313620 -28.63940 -53.60620 5
## 6 0.555183946 0.458392220 0.67010477 -28.63940 -53.60620 8
## 7 0.632996633 0.526586071 0.75900598 -28.38100 -53.30600 9
## 8 0.343283582 0.259437041 0.44870866 -28.38100 -53.30600 10
## 9 0.531468531 0.435618457 0.64584799 -28.38100 -53.30600 11
## 10 0.529010239 0.434516269 0.64154635 -28.38100 -53.30600 12
## 11 0.355932203 0.283674486 0.44309205 -28.38100 -53.30600 15
## 12 0.020202020 0.007980190 0.04135790 -28.38100 -53.30600 16
## 13 0.662116041 0.551575867 0.79303448 -27.35570 -52.76600 17
## 14 0.636666667 0.530312961 0.76248546 -27.35570 -52.76600 18
## 15 0.283333333 0.221419757 0.35865532 -27.35570 -52.76600 19
## 16 0.071428571 0.042027703 0.11314237 -27.35570 -52.76600 21
## 17 0.531135531 0.433269543 0.64840999 -27.35570 -52.76600 22
## 18 0.208754209 0.157402862 0.27235822 -27.35570 -52.76600 23
## 19 0.364583333 0.290378142 0.45420850 -28.51070 -50.92980 26
## 20 0.397923875 0.319444229 0.49236335 -28.51070 -50.92980 30
## 21 0.601423488 0.496134104 0.72685347 -27.95000 -54.75000 31
## 22 0.137123746 0.097524091 0.18766164 -27.87100 -54.48200 32
## 23 0.620000000 0.515616328 0.74356310 -27.87100 -54.48200 36
## 24 0.580000000 0.480377162 0.69811667 -27.75760 -54.48510 37
## 25 0.728301887 0.604381183 0.87604649 -27.75760 -54.48510 38
## 26 0.493103448 0.402620718 0.60117879 -27.65730 -54.44390 40
## 27 0.670033670 0.559247209 0.80106015 -27.55480 -54.53610 41
## 28 0.410958904 0.331160296 0.50678245 -28.02830 -54.34970 44
## 29 0.032727273 0.015593213 0.05976616 -27.94250 -52.92360 45
## 30 0.153583618 0.110785772 0.20783331 -27.94250 -52.92360 46
## 31 0.749152542 0.628750725 0.89131112 -27.94250 -52.92360 47
## 32 0.294736842 0.229758718 0.37403712 -27.94250 -52.92360 48
## 33 0.358974359 0.271462157 0.46914675 -27.94250 -52.92360 49
## 34 0.698996656 0.585163254 0.83343233 -28.27830 -52.76650 50
## 35 0.153571429 0.109913055 0.20921473 -28.27830 -52.76650 51
## 36 0.288590604 0.225758565 0.36501205 -28.27830 -52.76650 52
## 37 0.040133779 0.021292682 0.06821920 -28.27830 -52.76650 53
## 38 0.580536913 0.480543499 0.69915793 -28.27830 -52.76650 54
## 39 0.273049645 0.210862667 0.34931599 -28.27830 -52.76650 55
## 40 0.326599327 0.258473932 0.40901823 -28.27830 -52.76650 56
## 41 0.500000000 0.409508110 0.60784659 -28.27830 -52.76650 57
## 42 0.503448276 0.411675736 0.61299689 -28.27830 -52.76650 58
## 43 0.376271186 0.301336801 0.46646229 -28.27830 -52.76650 59
## 44 0.386666667 0.310958088 0.47755512 -28.27830 -52.76650 60
## 45 0.313333333 0.247305269 0.39328412 -28.37000 -51.09200 61
## 46 0.423208191 0.341961185 0.52064028 -28.28990 -53.50170 65
## 47 0.567567568 0.468828830 0.68483486 -28.28990 -53.50170 66
## 48 0.562264151 0.459077673 0.68605139 -28.63940 -53.60620 67
## 49 0.858620690 0.724532923 1.01678259 -28.63940 -53.60620 68
## 50 0.337579618 0.245114013 0.45751868 -28.63940 -53.60620 70
## 51 0.133047211 0.089722211 0.19032930 -28.63940 -53.60620 71
## 52 0.211805556 0.159273358 0.27702897 -28.63940 -53.60620 76
## 53 0.759450172 0.637085218 0.90405389 -28.63940 -53.60620 78
## 54 0.630872483 0.524877690 0.75636344 -28.42220 -53.65570 80
## 55 0.231543624 0.176857961 0.29883407 -28.42220 -53.65570 81
## 56 0.072625699 0.039314811 0.12235111 -28.42220 -53.65570 82
## 57 0.315972222 0.248349011 0.39814721 -27.89940 -53.31360 83
## 58 0.731543624 0.613743313 0.87057714 -27.89940 -53.31360 84
## 59 0.234875445 0.178228025 0.30495359 -27.89940 -53.31360 85
## 60 0.155234657 0.111080389 0.21153495 -27.89940 -53.31360 86
## 61 0.138513514 0.098494919 0.18960568 -27.89940 -53.31360 87
## 62 0.388888889 0.297245961 0.50362552 -27.89940 -53.31360 88
## 63 0.194630872 0.145527749 0.25570165 -27.89940 -53.31360 89
## 64 0.017421603 0.006211844 0.03781776 -27.89940 -53.31360 90
## 65 0.510067114 0.418617264 0.61892855 -28.26120 -52.40830 94
## 66 0.166666667 0.115869953 0.23289533 -28.27830 -52.76650 95
## 67 0.501779359 0.408886725 0.61298874 -28.27830 -52.76650 96
## 68 0.328719723 0.259457333 0.41271027 -28.27830 -52.76650 97
## 69 0.396610169 0.319030814 0.48980083 -28.27830 -52.76650 99
## 70 0.151658768 0.102669603 0.21652111 -28.27830 -52.76650 100
## 71 0.650000000 0.542075610 0.77761800 -28.27830 -52.76650 101
## 72 0.770270270 0.647606457 0.91498394 -28.27830 -52.76650 102
## 73 0.335616438 0.265744468 0.42018626 -28.12840 -52.30280 105
## 74 0.367697595 0.293428221 0.45728252 -28.12840 -52.30280 106
## 75 0.314487633 0.246533386 0.39722458 -28.12840 -52.30280 107
## 76 0.207482993 0.156093008 0.27122880 -28.12840 -52.30280 108
## 77 0.707482993 0.591766698 0.84430150 -28.00840 -52.23610 109
## 78 0.512110727 0.419115013 0.62309837 -28.00840 -52.23610 110
## 79 0.468013468 0.381625808 0.57114337 -28.00840 -52.23610 111
## 80 0.385135135 0.309161411 0.47646789 -28.00840 -52.23610 112
## 81 0.638795987 0.532027498 0.76513149 -28.00840 -52.23610 113
## 82 0.709030100 0.594027352 0.84480766 -27.63050 -52.23640 114
## 83 0.394648829 0.317796031 0.48686972 -27.63050 -52.23640 115
## 84 0.041322314 0.020494409 0.07359827 -27.63050 -52.23640 116
## 85 0.744107744 0.624665011 0.88507292 -27.63050 -52.23640 120
## 86 0.871621622 0.737306460 1.02974718 -28.12840 -52.30280 121
## 87 0.073529412 0.045210235 0.11266134 -28.26120 -52.40830 123
## 88 0.543071161 0.442681604 0.66353683 -28.38850 -53.92050 128
## 89 0.536912752 0.442186445 0.64951390 -28.38850 -53.92050 129
## 90 0.741935484 0.619271687 0.88747496 -28.38850 -53.92050 130
## 91 0.721088435 0.603776941 0.85974010 -28.37000 -51.09200 131
## 92 0.840000000 0.710108859 0.99284112 -28.45790 -52.82200 132
## 93 0.620000000 0.515616328 0.74356310 -28.45790 -52.82200 135
## 94 0.619607843 0.507216032 0.75456828 -28.45790 -52.82200 136
## 95 0.335593220 0.266047401 0.41968656 -28.45790 -52.82200 137
## 96 0.874149660 0.739124765 1.03319167 -28.45790 -52.82200 138
## 97 0.747368421 0.625253927 0.89197343 -28.21070 -51.52620 140
## 98 0.738007380 0.614172540 0.88532101 -28.21070 -51.52620 141
## 99 0.498305085 0.407882591 0.60611175 -28.06480 -52.01000 142
## 100 0.770000000 0.648122841 0.91362745 -28.28990 -53.50170 143
## 101 0.360544218 0.287564115 0.44855797 -28.28990 -53.50170 144
## 102 0.140425533 0.095812511 0.19903864 -28.28990 -53.50170 145
## 103 0.043636364 0.023132560 0.07425957 -28.37067 -52.98822 146
## 104 0.811447811 0.684228423 0.96135545 -28.64086 -52.85092 147
## 105 0.118644068 0.082144461 0.16590833 -28.28990 -53.50170 148
## 106 0.434782609 0.352819659 0.53280393 -28.63940 -53.60620 1
## 107 0.225255973 0.171092096 0.29212999 -28.63940 -53.60620 2
## 108 0.259515571 0.199957457 0.33257009 -28.63940 -53.60620 3
## 109 0.193771626 0.144129068 0.25574035 -28.63940 -53.60620 4
## 110 0.214046823 0.162048693 0.27830647 -28.63940 -53.60620 5
## 111 0.421404682 0.341133937 0.51750371 -28.63940 -53.60620 8
## 112 0.464646465 0.378679001 0.56729787 -28.38100 -53.30600 9
## 113 0.169154229 0.115641450 0.23978190 -28.38100 -53.30600 10
## 114 0.601398601 0.496959108 0.72562498 -28.38100 -53.30600 11
## 115 0.416382253 0.336010387 0.51281938 -28.38100 -53.30600 12
## 116 0.461016949 0.375238773 0.56352926 -28.38100 -53.30600 15
## 117 0.006734007 0.001116400 0.02092764 -28.38100 -53.30600 16
## 118 0.546075085 0.449491235 0.66100059 -27.35570 -52.76600 17
## 119 0.633333333 0.527373030 0.75870159 -27.35570 -52.76600 18
## 120 0.140000000 0.099958436 0.19099255 -27.35570 -52.76600 19
## 121 0.016806723 0.005186625 0.03947496 -27.35570 -52.76600 21
## 122 0.315018315 0.245876773 0.39949584 -27.35570 -52.76600 22
## 123 0.245791246 0.188937294 0.31553930 -27.35570 -52.76600 23
## 124 0.347222222 0.275342590 0.43421176 -28.51070 -50.92980 26
## 125 0.391003460 0.313428478 0.48441510 -28.51070 -50.92980 30
## 126 0.441281139 0.356123169 0.54364846 -27.95000 -54.75000 31
## 127 0.093645485 0.062169463 0.13530847 -27.87100 -54.48200 32
## 128 0.546666667 0.451050393 0.66020543 -27.87100 -54.48200 36
## 129 0.406666667 0.328392090 0.50046396 -27.75760 -54.48510 37
## 130 0.649056604 0.534936670 0.78546216 -27.75760 -54.48510 38
## 131 0.268965517 0.208140170 0.34340821 -27.65730 -54.44390 40
## 132 0.538720539 0.443628298 0.65177929 -27.55480 -54.53610 41
## 133 0.277397260 0.215561487 0.35290535 -28.02830 -54.34970 44
## 134 0.054545455 0.031030726 0.08839099 -27.94250 -52.92360 45
## 135 0.160409556 0.116463451 0.21592734 -27.94250 -52.92360 46
## 136 0.542372881 0.446540472 0.65635766 -27.94250 -52.92360 47
## 137 0.333333333 0.263000995 0.41868446 -27.94250 -52.92360 48
## 138 0.343589746 0.258558395 0.45087475 -27.94250 -52.92360 49
## 139 0.688963211 0.576301362 0.82205479 -28.27830 -52.76650 50
## 140 0.167857143 0.121762366 0.22619934 -28.27830 -52.76650 51
## 141 0.053691275 0.031148535 0.08575199 -28.27830 -52.76650 52
## 142 0.026755853 0.012138979 0.05037308 -28.27830 -52.76650 53
## 143 0.412751678 0.333460242 0.50777534 -28.27830 -52.76650 54
## 144 0.138297872 0.097460344 0.19075644 -28.27830 -52.76650 55
## 145 0.377104377 0.302291309 0.46708554 -28.27830 -52.76650 56
## 146 0.435810811 0.353339581 0.53452154 -28.27830 -52.76650 57
## 147 0.362068966 0.288430645 0.45097599 -28.27830 -52.76650 58
## 148 0.030508475 0.014544293 0.05566517 -28.27830 -52.76650 59
## 149 0.300000000 0.235787602 0.37790654 -28.27830 -52.76650 60
## 150 0.410000000 0.331300369 0.50427949 -28.37000 -51.09200 61
## 151 0.317406143 0.250111564 0.39902859 -28.28990 -53.50170 65
## 152 0.466216216 0.379919908 0.56928028 -28.28990 -53.50170 66
## 153 0.664150943 0.548152247 0.80272827 -28.63940 -53.60620 67
## 154 0.486206897 0.396586736 0.59329737 -28.63940 -53.60620 68
## 155 0.388535032 0.287051234 0.51916688 -28.63940 -53.60620 70
## 156 0.145922747 0.100080298 0.20600753 -28.63940 -53.60620 71
## 157 0.131944444 0.092617138 0.18253650 -28.63940 -53.60620 76
## 158 0.701030928 0.585529508 0.83773880 -28.63940 -53.60620 78
## 159 0.624161074 0.518962215 0.74874029 -28.42220 -53.65570 80
## 160 0.251677852 0.194059467 0.32224854 -28.42220 -53.65570 81
## 161 0.122905028 0.076766315 0.18693078 -28.42220 -53.65570 82
## 162 0.263888889 0.203604940 0.33779436 -27.89940 -53.31360 83
## 163 0.587248322 0.486450293 0.70678977 -27.89940 -53.31360 84
## 164 0.313167260 0.245179371 0.39601923 -27.89940 -53.31360 85
## 165 0.054151625 0.030809176 0.08774271 -27.89940 -53.31360 86
## 166 0.158783784 0.115305504 0.21368787 -27.89940 -53.31360 87
## 167 0.166666667 0.113299023 0.23734491 -27.89940 -53.31360 88
## 168 0.161073826 0.117344920 0.21619117 -27.89940 -53.31360 89
## 169 0.059233450 0.034934962 0.09351700 -27.89940 -53.31360 90
## 170 0.459731544 0.374509452 0.56149661 -28.26120 -52.40830 94
## 171 0.063063063 0.035047709 0.10408108 -28.27830 -52.76650 95
## 172 0.466192171 0.377826707 0.57222298 -28.27830 -52.76650 96
## 173 0.200692042 0.149962524 0.26387091 -28.27830 -52.76650 97
## 174 0.223728814 0.169958010 0.29009670 -28.27830 -52.76650 99
## 175 0.118483412 0.076380843 0.17564405 -28.27830 -52.76650 100
## 176 0.690000000 0.577390008 0.82298918 -28.27830 -52.76650 101
## 177 0.699324324 0.584923533 0.83454256 -28.27830 -52.76650 102
## 178 0.301369863 0.236178003 0.38065633 -28.12840 -52.30280 105
## 179 0.316151203 0.248819670 0.39788720 -28.12840 -52.30280 106
## 180 0.162544170 0.117558590 0.21955036 -28.12840 -52.30280 107
## 181 0.037414966 0.019254584 0.06492699 -28.12840 -52.30280 108
## 182 0.489795918 0.400288661 0.59659782 -28.00840 -52.23610 109
## 183 0.550173010 0.452476072 0.66653921 -28.00840 -52.23610 110
## 184 0.319865320 0.252649368 0.40125817 -28.00840 -52.23610 111
## 185 0.418918919 0.338595290 0.51518856 -28.00840 -52.23610 112
## 186 0.525083612 0.431940506 0.63583815 -28.00840 -52.23610 113
## 187 0.618729097 0.514336039 0.74234407 -27.63050 -52.23640 114
## 188 0.260869565 0.202023406 0.33278270 -27.63050 -52.23640 115
## 189 0.107438018 0.070011137 0.15775239 -27.63050 -52.23640 116
## 190 0.518518519 0.425891643 0.62876212 -27.63050 -52.23640 120
## 191 0.611486486 0.507473253 0.73479125 -28.12840 -52.30280 121
## 192 0.058823529 0.034090904 0.09408423 -28.26120 -52.40830 123
## 193 0.516853933 0.419825898 0.63345294 -28.38850 -53.92050 128
## 194 0.543624161 0.448083118 0.65715586 -28.38850 -53.92050 129
## 195 0.655913978 0.543616632 0.78950129 -28.38850 -53.92050 130
## 196 0.663265306 0.552761836 0.79409761 -28.37000 -51.09200 131
## 197 0.853333333 0.721924336 1.00792082 -28.45790 -52.82200 132
## 198 0.640000000 0.533253188 0.76626903 -28.45790 -52.82200 135
## 199 0.643137255 0.527747078 0.78157080 -28.45790 -52.82200 136
## 200 0.220338983 0.167076637 0.28613931 -28.45790 -52.82200 137
## 201 0.435374150 0.352702946 0.53438803 -28.45790 -52.82200 138
## 202 0.736842105 0.615973810 0.88001097 -28.21070 -51.52620 140
## 203 0.627306273 0.517038578 0.75895162 -28.21070 -51.52620 141
## 204 0.410169492 0.330842912 0.50534377 -28.06480 -52.01000 142
## 205 0.873333333 0.739652273 1.03053564 -28.28990 -53.50170 143
## 206 0.462585034 0.376476760 0.56551197 -28.28990 -53.50170 144
## 207 0.114893617 0.075418998 0.16780557 -28.28990 -53.50170 145
## 208 0.061818182 0.036441035 0.09766599 -28.37067 -52.98822 146
## 209 0.787878788 0.663372275 0.93466552 -28.64086 -52.85092 147
## 210 0.189830508 0.141258263 0.25040876 -28.28990 -53.50170 148
## fungicide year state
## 1 Azoxystrobin 2007 RS
## 2 Azoxystrobin 2007 RS
## 3 Azoxystrobin 2007 RS
## 4 Azoxystrobin 2007 RS
## 5 Azoxystrobin 2007 RS
## 6 Azoxystrobin 2007 RS
## 7 Azoxystrobin 2007 RS
## 8 Azoxystrobin 2007 RS
## 9 Azoxystrobin 2007 RS
## 10 Azoxystrobin 2007 RS
## 11 Azoxystrobin 2007 RS
## 12 Azoxystrobin 2007 RS
## 13 Azoxystrobin 2007 RS
## 14 Azoxystrobin 2007 RS
## 15 Azoxystrobin 2007 RS
## 16 Azoxystrobin 2007 RS
## 17 Azoxystrobin 2007 RS
## 18 Azoxystrobin 2007 RS
## 19 Azoxystrobin 2007 RS
## 20 Azoxystrobin 2007 RS
## 21 Azoxystrobin 2008 RS
## 22 Azoxystrobin 2008 RS
## 23 Azoxystrobin 2008 RS
## 24 Azoxystrobin 2008 RS
## 25 Azoxystrobin 2008 RS
## 26 Azoxystrobin 2008 RS
## 27 Azoxystrobin 2008 RS
## 28 Azoxystrobin 2008 RS
## 29 Azoxystrobin 2008 RS
## 30 Azoxystrobin 2008 RS
## 31 Azoxystrobin 2008 RS
## 32 Azoxystrobin 2008 RS
## 33 Azoxystrobin 2008 RS
## 34 Azoxystrobin 2008 RS
## 35 Azoxystrobin 2008 RS
## 36 Azoxystrobin 2008 RS
## 37 Azoxystrobin 2008 RS
## 38 Azoxystrobin 2008 RS
## 39 Azoxystrobin 2008 RS
## 40 Azoxystrobin 2008 RS
## 41 Azoxystrobin 2008 RS
## 42 Azoxystrobin 2008 RS
## 43 Azoxystrobin 2008 RS
## 44 Azoxystrobin 2008 RS
## 45 Azoxystrobin 2009 RS
## 46 Azoxystrobin 2009 RS
## 47 Azoxystrobin 2009 RS
## 48 Azoxystrobin 2009 RS
## 49 Azoxystrobin 2009 RS
## 50 Azoxystrobin 2009 RS
## 51 Azoxystrobin 2009 RS
## 52 Azoxystrobin 2009 RS
## 53 Azoxystrobin 2009 RS
## 54 Azoxystrobin 2009 RS
## 55 Azoxystrobin 2009 RS
## 56 Azoxystrobin 2009 RS
## 57 Azoxystrobin 2009 RS
## 58 Azoxystrobin 2009 RS
## 59 Azoxystrobin 2009 RS
## 60 Azoxystrobin 2009 RS
## 61 Azoxystrobin 2009 RS
## 62 Azoxystrobin 2009 RS
## 63 Azoxystrobin 2009 RS
## 64 Azoxystrobin 2009 RS
## 65 Azoxystrobin 2010 RS
## 66 Azoxystrobin 2010 RS
## 67 Azoxystrobin 2010 RS
## 68 Azoxystrobin 2010 RS
## 69 Azoxystrobin 2010 RS
## 70 Azoxystrobin 2010 RS
## 71 Azoxystrobin 2010 RS
## 72 Azoxystrobin 2010 RS
## 73 Azoxystrobin 2010 RS
## 74 Azoxystrobin 2010 RS
## 75 Azoxystrobin 2010 RS
## 76 Azoxystrobin 2010 RS
## 77 Azoxystrobin 2010 RS
## 78 Azoxystrobin 2010 RS
## 79 Azoxystrobin 2010 RS
## 80 Azoxystrobin 2010 RS
## 81 Azoxystrobin 2010 RS
## 82 Azoxystrobin 2010 RS
## 83 Azoxystrobin 2010 RS
## 84 Azoxystrobin 2010 RS
## 85 Azoxystrobin 2010 RS
## 86 Azoxystrobin 2010 RS
## 87 Azoxystrobin 2011 RS
## 88 Azoxystrobin 2011 RS
## 89 Azoxystrobin 2011 RS
## 90 Azoxystrobin 2011 RS
## 91 Azoxystrobin 2011 RS
## 92 Azoxystrobin 2011 RS
## 93 Azoxystrobin 2011 RS
## 94 Azoxystrobin 2011 RS
## 95 Azoxystrobin 2011 RS
## 96 Azoxystrobin 2011 RS
## 97 Azoxystrobin 2011 RS
## 98 Azoxystrobin 2011 RS
## 99 Azoxystrobin 2011 RS
## 100 Azoxystrobin 2011 RS
## 101 Azoxystrobin 2011 RS
## 102 Azoxystrobin 2011 RS
## 103 Azoxystrobin 2011 RS
## 104 Azoxystrobin 2011 RS
## 105 Azoxystrobin 2011 RS
## 106 Pyraclostrobin 2007 RS
## 107 Pyraclostrobin 2007 RS
## 108 Pyraclostrobin 2007 RS
## 109 Pyraclostrobin 2007 RS
## 110 Pyraclostrobin 2007 RS
## 111 Pyraclostrobin 2007 RS
## 112 Pyraclostrobin 2007 RS
## 113 Pyraclostrobin 2007 RS
## 114 Pyraclostrobin 2007 RS
## 115 Pyraclostrobin 2007 RS
## 116 Pyraclostrobin 2007 RS
## 117 Pyraclostrobin 2007 RS
## 118 Pyraclostrobin 2007 RS
## 119 Pyraclostrobin 2007 RS
## 120 Pyraclostrobin 2007 RS
## 121 Pyraclostrobin 2007 RS
## 122 Pyraclostrobin 2007 RS
## 123 Pyraclostrobin 2007 RS
## 124 Pyraclostrobin 2007 RS
## 125 Pyraclostrobin 2007 RS
## 126 Pyraclostrobin 2008 RS
## 127 Pyraclostrobin 2008 RS
## 128 Pyraclostrobin 2008 RS
## 129 Pyraclostrobin 2008 RS
## 130 Pyraclostrobin 2008 RS
## 131 Pyraclostrobin 2008 RS
## 132 Pyraclostrobin 2008 RS
## 133 Pyraclostrobin 2008 RS
## 134 Pyraclostrobin 2008 RS
## 135 Pyraclostrobin 2008 RS
## 136 Pyraclostrobin 2008 RS
## 137 Pyraclostrobin 2008 RS
## 138 Pyraclostrobin 2008 RS
## 139 Pyraclostrobin 2008 RS
## 140 Pyraclostrobin 2008 RS
## 141 Pyraclostrobin 2008 RS
## 142 Pyraclostrobin 2008 RS
## 143 Pyraclostrobin 2008 RS
## 144 Pyraclostrobin 2008 RS
## 145 Pyraclostrobin 2008 RS
## 146 Pyraclostrobin 2008 RS
## 147 Pyraclostrobin 2008 RS
## 148 Pyraclostrobin 2008 RS
## 149 Pyraclostrobin 2008 RS
## 150 Pyraclostrobin 2009 RS
## 151 Pyraclostrobin 2009 RS
## 152 Pyraclostrobin 2009 RS
## 153 Pyraclostrobin 2009 RS
## 154 Pyraclostrobin 2009 RS
## 155 Pyraclostrobin 2009 RS
## 156 Pyraclostrobin 2009 RS
## 157 Pyraclostrobin 2009 RS
## 158 Pyraclostrobin 2009 RS
## 159 Pyraclostrobin 2009 RS
## 160 Pyraclostrobin 2009 RS
## 161 Pyraclostrobin 2009 RS
## 162 Pyraclostrobin 2009 RS
## 163 Pyraclostrobin 2009 RS
## 164 Pyraclostrobin 2009 RS
## 165 Pyraclostrobin 2009 RS
## 166 Pyraclostrobin 2009 RS
## 167 Pyraclostrobin 2009 RS
## 168 Pyraclostrobin 2009 RS
## 169 Pyraclostrobin 2009 RS
## 170 Pyraclostrobin 2010 RS
## 171 Pyraclostrobin 2010 RS
## 172 Pyraclostrobin 2010 RS
## 173 Pyraclostrobin 2010 RS
## 174 Pyraclostrobin 2010 RS
## 175 Pyraclostrobin 2010 RS
## 176 Pyraclostrobin 2010 RS
## 177 Pyraclostrobin 2010 RS
## 178 Pyraclostrobin 2010 RS
## 179 Pyraclostrobin 2010 RS
## 180 Pyraclostrobin 2010 RS
## 181 Pyraclostrobin 2010 RS
## 182 Pyraclostrobin 2010 RS
## 183 Pyraclostrobin 2010 RS
## 184 Pyraclostrobin 2010 RS
## 185 Pyraclostrobin 2010 RS
## 186 Pyraclostrobin 2010 RS
## 187 Pyraclostrobin 2010 RS
## 188 Pyraclostrobin 2010 RS
## 189 Pyraclostrobin 2010 RS
## 190 Pyraclostrobin 2010 RS
## 191 Pyraclostrobin 2010 RS
## 192 Pyraclostrobin 2011 RS
## 193 Pyraclostrobin 2011 RS
## 194 Pyraclostrobin 2011 RS
## 195 Pyraclostrobin 2011 RS
## 196 Pyraclostrobin 2011 RS
## 197 Pyraclostrobin 2011 RS
## 198 Pyraclostrobin 2011 RS
## 199 Pyraclostrobin 2011 RS
## 200 Pyraclostrobin 2011 RS
## 201 Pyraclostrobin 2011 RS
## 202 Pyraclostrobin 2011 RS
## 203 Pyraclostrobin 2011 RS
## 204 Pyraclostrobin 2011 RS
## 205 Pyraclostrobin 2011 RS
## 206 Pyraclostrobin 2011 RS
## 207 Pyraclostrobin 2011 RS
## 208 Pyraclostrobin 2011 RS
## 209 Pyraclostrobin 2011 RS
## 210 Pyraclostrobin 2011 RSBoxplot (all isolates)
box_1=results_rs_1 %>%
ggplot(aes(fungicide, mean), color=lower>0.5)+
ylim(0,1)+
geom_jitter(size=2, width = 0.2, color="gray")+
geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
scale_color_colorblind()+
geom_hline(yintercept = 0.5, color="red")+
#annotate("text", x=2.5,y=5.5, label="DD", size=4, color="red")+
#facet_wrap("Population")+
theme_minimal()+
labs(y = "",
x="")
box_1
#ggsave("img/box_AZOX.png", dpi = 500, height = 3.5, width = 5)Less sensitive year, Pyra, RS
results_rs_2 <- results_rs_1 %>%
filter(fungicide== "Pyraclostrobin")
dd_1=results_rs_2 %>%
ggplot(aes(year, mean, group=lower, color=lower>0.5))+
geom_hline(yintercept = .50, color="red")+
scale_color_colorblind()+
scale_y_continuous(limits = c(0, 1.2))+
geom_jitter(position= position_dodge(width = 1))+
geom_errorbar(aes(ymin = lower, ymax = upper, color = lower>0.5), width=1, position= position_dodge(width = 1), size=0.3)+
#facet_wrap(~fungicide, ncol = 1)+
theme_minimal(base_size = 18)+
labs(x="", y="", color="LL IC95")+
theme(legend.position = "none")
dd_1
ggsave("img/dd_rs_2.png",dpi= 600, height = 8, width = 8)Less sensitive, year, Azox, RS
results_rs_3 <- results_rs_1 %>%
filter(fungicide== "Azoxystrobin")
dd_2=results_rs_3 %>%
ggplot(aes(year, mean, group=lower, color=lower>0.5))+
geom_hline(yintercept = .50, color="red")+
scale_color_colorblind()+
scale_y_continuous(limits = c(0, 1.2))+
geom_jitter(position= position_dodge(width = 1))+
geom_errorbar(aes(ymin = lower, ymax = upper, color = lower>0.5), width=1, position= position_dodge(width = 1), size=0.3)+
theme_minimal(base_size = 18)+
labs(x="Year", y="", color="LL IC95")+
theme(legend.position = "none")
dd_2
Proportion by year
results_freq<-results_rs_1 %>%
group_by( fungicide, year) %>%
mutate(n_isolates=length(isolate)) %>%
filter(lower>0.5) %>%
summarise(count=n(),
prop=(count/mean(n_isolates))*100)## `summarise()` has grouped output by 'fungicide'. You can override using the `.groups` argument.results_freq## # A tibble: 10 × 4
## # Groups: fungicide [2]
## fungicide year count prop
## <chr> <chr> <int> <dbl>
## 1 Azoxystrobin 2007 3 15
## 2 Azoxystrobin 2008 5 20.8
## 3 Azoxystrobin 2009 4 20
## 4 Azoxystrobin 2010 7 31.8
## 5 Azoxystrobin 2011 10 52.6
## 6 Pyraclostrobin 2007 1 5
## 7 Pyraclostrobin 2008 2 8.33
## 8 Pyraclostrobin 2009 3 15
## 9 Pyraclostrobin 2010 4 18.2
## 10 Pyraclostrobin 2011 9 47.4Less sensitive, year, RS
dd_2+dd_1+plot_annotation(tag_levels = list(c("A", "B")))+plot_layout(ncol = 2)+
labs(y= "")
ggsave("img/dd_all_all.png",dpi= 500, height = 8, width = 14)Load data - Paraná
data_load_pr = gsheet2tbl("https://docs.google.com/spreadsheets/d/1xaHgZCiO85EwuowMlGKlvO1tlwG2SleaBG-L-5x4Enw/edit#gid=29082389") %>%
mutate(Fungicide = factor(Fungicide, levels = c("Control", "Azoxtrobin","Pyraclostrobin" ))) %>%
filter(!is.na(conidia))
data_load_pr## # A tibble: 1,332 × 9
## Isolate Year Host State Repetição Trial Fungicide Dose conidia
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <fct> <dbl> <dbl>
## 1 6001 2018 Wheat PR 1 1 Control 0 50
## 2 6001 2018 Wheat PR 2 1 Control 0 41
## 3 6001 2018 Wheat PR 3 1 Control 0 46
## 4 6001 2018 Wheat PR 1 1 Pyraclostrobin 0.5 25
## 5 6001 2018 Wheat PR 2 1 Pyraclostrobin 0.5 25
## 6 6001 2018 Wheat PR 3 1 Pyraclostrobin 0.5 22
## 7 6001 2018 Wheat PR 1 1 Azoxtrobin 5 41
## 8 6001 2018 Wheat PR 2 1 Azoxtrobin 5 42
## 9 6001 2018 Wheat PR 3 1 Azoxtrobin 5 43
## 10 6014 2018 Wheat PR 1 1 Control 0 40
## # … with 1,322 more rowsvisualization
data_load_pr %>%
ggplot(aes(Fungicide,conidia))+
geom_jitter()
Single isolate
single_pr = data_load_pr %>%
filter(Isolate == 6040)
single_pr %>%
ggplot(aes(Fungicide,conidia, color = factor(Trial)))+
geom_jitter(height = 0)
Glm
glm_single_pr = glm(conidia ~ Fungicide , family = poisson, data = single_pr)
summary(glm_single_pr)##
## Call:
## glm(formula = conidia ~ Fungicide, family = poisson, data = single_pr)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9446 -0.7474 -0.2246 0.5318 2.8338
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.83586 0.05998 63.957 < 2e-16 ***
## FungicideAzoxtrobin -0.43466 0.09567 -4.543 5.54e-06 ***
## FungicidePyraclostrobin -1.62029 0.14758 -10.979 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 189.736 on 17 degrees of freedom
## Residual deviance: 25.854 on 15 degrees of freedom
## AIC: 121.04
##
## Number of Fisher Scoring iterations: 4coef(glm_single_pr)[2:3]## FungicideAzoxtrobin FungicidePyraclostrobin
## -0.4346643 -1.6202879confint(glm_single_pr)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 3.7159586 3.9511505
## FungicideAzoxtrobin -0.6235703 -0.2482721
## FungicidePyraclostrobin -1.9194786 -1.3398278azox_single_pr = c(exp(coef(glm_single_pr)[2]), exp(confint(glm_single_pr)[2,]))## Waiting for profiling to be done...pyra_single_pr = c(exp(coef(glm_single_pr)[3]), exp(confint(glm_single_pr)[3,]))## Waiting for profiling to be done...Data frame Paraná
isolates = unique(data_load_pr$Isolate)
azox_all_pr = NULL
pyra_all_pr = NULL
for(i in 1:length(isolates)){
single_df_pr = data_load_pr %>%
filter(Isolate == isolates[i])
glm_single_pr = glm(conidia ~ Fungicide , family = poisson, data = single_df_pr)
summary(glm_single_pr)
# coef(glm_single_pr)[2:3]
# confint(glm_single_pr)
azox_single_pr = c(mean = as.numeric(exp(coef(glm_single_pr)[2])), exp(confint(glm_single_pr)[2,]), isol = isolates[i], year = unique(single_df_pr$Year), state= (single_df_pr$State))
pyra_single_pr = c(mean = as.numeric(exp(coef(glm_single_pr)[3])), exp(confint(glm_single_pr)[3,]), isol = isolates[i], year = unique(single_df_pr$Year), state= (single_df_pr$State))
azox_all_pr = rbind(azox_all_pr, azox_single_pr)
pyra_all_pr = rbind(pyra_all_pr, pyra_single_pr)
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## Waiting for profiling to be done...## Warning in rbind(azox_all_pr, azox_single_pr): number of columns of result is
## not a multiple of vector length (arg 2)## Warning in rbind(pyra_all_pr, pyra_single_pr): number of columns of result is
## not a multiple of vector length (arg 2)## Waiting for profiling to be done...
## Waiting for profiling to be done...## Warning in rbind(azox_all_pr, azox_single_pr): number of columns of result is
## not a multiple of vector length (arg 2)
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## Waiting for profiling to be done...results_pr = bind_rows(
as.data.frame(azox_all_pr) %>%
mutate(fungicide = "Azoxystrobin"),
as.data.frame(pyra_all_pr) %>%
mutate(fungicide = "Pyraclostrobin")
)
results_pr## mean 2.5 % 97.5 %
## azox_single_pr 0.736462093862819 0.614049760285394 0.881814217349013
## azox_single_pr.1 0.727611940298508 0.604412481562192 0.874354426807635
## azox_single_pr.2 0.175675675675676 0.129429060728632 0.233641642151195
## azox_single_pr.3 0.732441471571906 0.614718463267838 0.871341884812485
## azox_single_pr.4 0.56 0.462776514457351 0.675374512904487
## azox_single_pr.5 0.647482014388492 0.536027214974515 0.780147627398873
## azox_single_pr.6 0.193979933110368 0.145051146235219 0.25482503265163
## azox_single_pr.7 0.144278606965175 0.0957742505671896 0.209259086915127
## azox_single_pr.8 0.667785234899332 0.557434017627934 0.798269538468759
## azox_single_pr.9 0.474747474747475 0.387521110641593 0.578832684123637
## azox_single_pr.10 0.302083333333335 0.236384539726069 0.3820858411134
## azox_single_pr.11 0.315068493150685 0.247989654076369 0.396483238995149
## azox_single_pr.12 0.34 0.270396327333935 0.423983553387451
## azox_single_pr.13 0.329931972789304 0.261041866866901 0.413320194140721
## azox_single_pr.14 0.585284280936456 0.48487599560576 0.704339320490915
## azox_single_pr.15 0.224489796117301 0.170523167011626 0.291109800244756
## azox_single_pr.16 0.560402684563758 0.462831991284609 0.676253555502486
## azox_single_pr.17 0.724489795918395 0.606780106052922 0.863599141636898
## azox_single_pr.18 0.434931506849315 0.352058998137144 0.534251184295637
## azox_single_pr.19 0.321070234113712 0.253895834887262 0.402347145739479
## azox_single_pr.20 0.65979381443299 0.549181943089545 0.790883428764812
## azox_single_pr.21 0.503448275922299 0.411675735982017 0.612996885821208
## azox_single_pr.22 0.783505154639176 0.658333147162011 0.93134099094598
## azox_single_pr.23 0.280155642023346 0.214255465133445 0.361651800819593
## azox_single_pr.24 0.447098976286417 0.362810883451566 0.547991498972251
## azox_single_pr.25 0.181184669168706 0.133404495677388 0.241151545939536
## azox_single_pr.26 0.308474576271187 0.242606670194475 0.38841719076349
## azox_single_pr.27 0.213559322033898 0.161320993023581 0.278217416081666
## azox_single_pr.28 0.282229965156794 0.219223023262169 0.359234776330337
## azox_single_pr.29 0.270758122780608 0.208403376761355 0.347404629887614
## azox_single_pr.30 0.218309859160396 0.16444596695745 0.285162092033507
## azox_single_pr.31 0.48135593220339 0.393036280033232 0.586764289192992
## azox_single_pr.32 0.417391304347827 0.32756133966138 0.527727167413352
## azox_single_pr.33 0.925423728813559 0.784763966067886 1.09091259640605
## azox_single_pr.34 0.22 0.167187555846748 0.28513503826905
## azox_single_pr.35 0.759999999999999 0.639274441802317 0.902304466289692
## azox_single_pr.36 0.328859060402814 0.260533719815264 0.411468805730865
## azox_single_pr.37 0.367892976588629 0.294507893053458 0.456185949936581
## azox_single_pr.38 0.430976430980662 0.349243948133924 0.528809852375624
## azox_single_pr.39 0.555183946488294 0.458392220254799 0.670104770263569
## azox_single_pr.40 0.608108108108108 0.50449849805326 0.730950563032375
## azox_single_pr.41 0.418060200668897 0.338214189992631 0.513676968913334
## azox_single_pr.42 0.177852349129064 0.131394672092216 0.23598806795972
## azox_single_pr.43 0.411552346570399 0.329738135746453 0.510273158493164
## azox_single_pr.44 0.138047138047152 0.0981691681758914 0.188953208994231
## azox_single_pr.45 0.11 0.0753783343018091 0.155057772265107
## azox_single_pr.46 0.432885906040269 0.351037194286136 0.530814311884507
## azox_single_pr.47 0.109215017066751 0.0743883195542293 0.154713895579635
## azox_single_pr.48 0.0166666666666667 0.00594857537715076 0.0361144096059694
## azox_single_pr.49 0.441767068273094 0.356835408979862 0.544419731321839
## azox_single_pr.50 0.685810810810811 0.57299580900304 0.819208502323291
## azox_single_pr.51 0.797153024911032 0.668338910374552 0.949690024337805
## azox_single_pr.52 0.633333333334409 0.527373029681431 0.758701592633516
## azox_single_pr.53 0.353333333333334 0.281967024718939 0.439308095772046
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## isol year state1 state2 state3 state4 state5 state6 state7
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## state8 state9 state10 state11 state12 state13 state14 state15
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## state16 state17 state18 fungicide
## azox_single_pr PR PR PR Azoxystrobin
## azox_single_pr.1 PR PR PR Azoxystrobin
## azox_single_pr.2 PR PR PR Azoxystrobin
## azox_single_pr.3 PR PR PR Azoxystrobin
## azox_single_pr.4 PR PR PR Azoxystrobin
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## azox_single_pr.48 PR PR PR Azoxystrobin
## azox_single_pr.49 PR PR 0.441767068273094 Azoxystrobin
## azox_single_pr.50 PR PR PR Azoxystrobin
## azox_single_pr.51 PR PR PR Azoxystrobin
## azox_single_pr.52 PR PR PR Azoxystrobin
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## azox_single_pr.72 PR PR PR Azoxystrobin
## azox_single_pr.73 PR PR PR Azoxystrobin
## pyra_single_pr PR PR PR Pyraclostrobin
## pyra_single_pr.1 PR PR PR Pyraclostrobin
## pyra_single_pr.2 PR PR PR Pyraclostrobin
## pyra_single_pr.3 PR PR PR Pyraclostrobin
## pyra_single_pr.4 PR PR PR Pyraclostrobin
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## pyra_single_pr.49 PR PR 0.297858099062918 Pyraclostrobin
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## pyra_single_pr.52 PR PR PR Pyraclostrobin
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## pyra_single_pr.68 PR PR PR Pyraclostrobin
## pyra_single_pr.69 PR PR PR Pyraclostrobin
## pyra_single_pr.70 PR PR PR Pyraclostrobin
## pyra_single_pr.71 PR PR PR Pyraclostrobin
## pyra_single_pr.72 PR PR PR Pyraclostrobin
## pyra_single_pr.73 PR PR PR PyraclostrobinData frame reorganization
results_pr_1<-results_pr %>%
summarise(mean=as.numeric(mean),
lower=as.numeric(`2.5 %`),
upper= as.numeric(`97.5 %`),
isolate= as.numeric(isol),
fungicide=fungicide,
year=year,
state= state1)
head(results_pr_1)## mean lower upper isolate fungicide year state
## 1 0.7364621 0.6140498 0.8818142 6001 Azoxystrobin 2018 PR
## 2 0.7276119 0.6044125 0.8743544 6014 Azoxystrobin 2018 PR
## 3 0.1756757 0.1294291 0.2336416 6015 Azoxystrobin 2018 PR
## 4 0.7324415 0.6147185 0.8713419 6020 Azoxystrobin 2018 PR
## 5 0.5600000 0.4627765 0.6753745 6022 Azoxystrobin 2018 PR
## 6 0.6474820 0.5360272 0.7801476 6040 Azoxystrobin 2018 PRBoxplot (all isolates)
box_2=results_pr_1 %>%
ggplot(aes(fungicide, mean), color=lower>0.5)+
ylim(0,1)+
geom_jitter(size=2, width = 0.2, color="gray")+
geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
scale_color_colorblind()+
geom_hline(yintercept = 0.5, color="red")+
#annotate("text", x=2.5,y=0.6, label="DD", size=4, color="red")+
#facet_wrap("Population")+
theme_minimal()+
labs(y = "",
x="Fungicides")
box_2
#ggsave("img/box_AZOX.png", dpi = 500, height = 3.5, width = 5)box_1+box_2+plot_annotation(tag_levels = list(c("A", "B")))+plot_layout(ncol = 2)+
labs(y= "")
ggsave("img/box_all.png",dpi= 500, height = 8, width = 12)Join data frame
Here we loaded in the same data frame all values for the discriminatory dose for both fungicides.
DD_ALL= full_join(results_rs_1, results_pr_1) ## Joining, by = c("mean", "lower", "upper", "isolate", "fungicide", "year", "state")head(DD_ALL)## mean lower upper lat long isolate fungicide year
## 1 0.3143813 0.24811116 0.3946388 -28.6394 -53.6062 1 Azoxystrobin 2007
## 2 0.2593857 0.20021332 0.3318673 -28.6394 -53.6062 2 Azoxystrobin 2007
## 3 0.2906574 0.22665879 0.3687068 -28.6394 -53.6062 3 Azoxystrobin 2007
## 4 0.0899654 0.05878903 0.1316153 -28.6394 -53.6062 4 Azoxystrobin 2007
## 5 0.3478261 0.27707870 0.4331362 -28.6394 -53.6062 5 Azoxystrobin 2007
## 6 0.5551839 0.45839222 0.6701048 -28.6394 -53.6062 8 Azoxystrobin 2007
## state
## 1 RS
## 2 RS
## 3 RS
## 4 RS
## 5 RS
## 6 RSBoxplot DD
DD_ALL %>%
ggplot(aes(state, mean, fill= fungicide))+
ylim(0,1)+
geom_boxplot(width=0.5, outlier.color = NA, size =1, alpha=0.3)+
geom_jitter(size=1, width = 0.2, color="black")+
scale_fill_manual(values = c("15","gray90"))+
#scale_color_colorblind()+
geom_hline(yintercept = 0.5, color="red")+
#annotate("text", x=2.5,y=0.6, label="DD", size=4, color="red")+
facet_wrap(~fungicide)+
theme_minimal()+
theme(legend.position = "none")+
labs(y = "Prop of germinated conidia on the DD in relation to control ",
x="Collection")
ggsave("img/box_all.png", dpi = 500, height = 6, width = 6)